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        <a href="peach-module.html">Package&nbsp;peach</a> ::
        <a href="peach.optm-module.html">Package&nbsp;optm</a> ::
        Module&nbsp;multivar
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<h1 class="epydoc">Source Code for <a href="peach.optm.multivar-module.html">Module peach.optm.multivar</a></h1>
<pre class="py-src">
<a name="L1"></a><tt class="py-lineno">  1</tt>  <tt class="py-line"><tt class="py-comment">################################################################################</tt> </tt>
<a name="L2"></a><tt class="py-lineno">  2</tt>  <tt class="py-line"><tt class="py-comment"># Peach - Computational Intelligence for Python</tt> </tt>
<a name="L3"></a><tt class="py-lineno">  3</tt>  <tt class="py-line"><tt class="py-comment"># Jose Alexandre Nalon</tt> </tt>
<a name="L4"></a><tt class="py-lineno">  4</tt>  <tt class="py-line"><tt class="py-comment">#</tt> </tt>
<a name="L5"></a><tt class="py-lineno">  5</tt>  <tt class="py-line"><tt class="py-comment"># This file: optm/multivar.py</tt> </tt>
<a name="L6"></a><tt class="py-lineno">  6</tt>  <tt class="py-line"><tt class="py-comment"># Gradient and multivariable search methods</tt> </tt>
<a name="L7"></a><tt class="py-lineno">  7</tt>  <tt class="py-line"><tt class="py-comment">################################################################################</tt> </tt>
<a name="L8"></a><tt class="py-lineno">  8</tt>  <tt class="py-line"> </tt>
<a name="L9"></a><tt class="py-lineno">  9</tt>  <tt class="py-line"><tt class="py-comment"># Doc string, reStructuredText formatted:</tt> </tt>
<a name="L10"></a><tt class="py-lineno"> 10</tt>  <tt class="py-line"><tt id="link-0" class="py-name" targets="Variable peach.__doc__=peach-module.html#__doc__,Variable peach.fuzzy.__doc__=peach.fuzzy-module.html#__doc__,Variable peach.fuzzy.base.__doc__=peach.fuzzy.base-module.html#__doc__,Variable peach.fuzzy.cmeans.__doc__=peach.fuzzy.cmeans-module.html#__doc__,Variable peach.fuzzy.control.__doc__=peach.fuzzy.control-module.html#__doc__,Variable peach.fuzzy.defuzzy.__doc__=peach.fuzzy.defuzzy-module.html#__doc__,Variable peach.fuzzy.mf.__doc__=peach.fuzzy.mf-module.html#__doc__,Variable peach.fuzzy.norms.__doc__=peach.fuzzy.norms-module.html#__doc__,Variable peach.ga.__doc__=peach.ga-module.html#__doc__,Variable peach.ga.base.__doc__=peach.ga.base-module.html#__doc__,Variable peach.ga.chromosome.__doc__=peach.ga.chromosome-module.html#__doc__,Variable peach.ga.crossover.__doc__=peach.ga.crossover-module.html#__doc__,Variable peach.ga.fitness.__doc__=peach.ga.fitness-module.html#__doc__,Variable peach.ga.mutation.__doc__=peach.ga.mutation-module.html#__doc__,Variable peach.ga.selection.__doc__=peach.ga.selection-module.html#__doc__,Variable peach.nn.__doc__=peach.nn-module.html#__doc__,Variable peach.nn.af.__doc__=peach.nn.af-module.html#__doc__,Variable peach.nn.base.__doc__=peach.nn.base-module.html#__doc__,Variable peach.nn.kmeans.__doc__=peach.nn.kmeans-module.html#__doc__,Variable peach.nn.lrules.__doc__=peach.nn.lrules-module.html#__doc__,Variable peach.nn.mem.__doc__=peach.nn.mem-module.html#__doc__,Variable peach.nn.nnet.__doc__=peach.nn.nnet-module.html#__doc__,Variable peach.nn.rbfn.__doc__=peach.nn.rbfn-module.html#__doc__,Variable peach.optm.__doc__=peach.optm-module.html#__doc__,Variable peach.optm.base.__doc__=peach.optm.base-module.html#__doc__,Variable peach.optm.linear.__doc__=peach.optm.linear-module.html#__doc__,Variable peach.optm.multivar.__doc__=peach.optm.multivar-module.html#__doc__,Variable peach.optm.quasinewton.__doc__=peach.optm.quasinewton-module.html#__doc__,Variable peach.optm.stochastic.__doc__=peach.optm.stochastic-module.html#__doc__,Variable peach.pso.__doc__=peach.pso-module.html#__doc__,Variable peach.pso.acc.__doc__=peach.pso.acc-module.html#__doc__,Variable peach.pso.base.__doc__=peach.pso.base-module.html#__doc__,Variable peach.sa.__doc__=peach.sa-module.html#__doc__,Variable peach.sa.base.__doc__=peach.sa.base-module.html#__doc__,Variable peach.sa.neighbor.__doc__=peach.sa.neighbor-module.html#__doc__"><a title="peach.__doc__
peach.fuzzy.__doc__
peach.fuzzy.base.__doc__
peach.fuzzy.cmeans.__doc__
peach.fuzzy.control.__doc__
peach.fuzzy.defuzzy.__doc__
peach.fuzzy.mf.__doc__
peach.fuzzy.norms.__doc__
peach.ga.__doc__
peach.ga.base.__doc__
peach.ga.chromosome.__doc__
peach.ga.crossover.__doc__
peach.ga.fitness.__doc__
peach.ga.mutation.__doc__
peach.ga.selection.__doc__
peach.nn.__doc__
peach.nn.af.__doc__
peach.nn.base.__doc__
peach.nn.kmeans.__doc__
peach.nn.lrules.__doc__
peach.nn.mem.__doc__
peach.nn.nnet.__doc__
peach.nn.rbfn.__doc__
peach.optm.__doc__
peach.optm.base.__doc__
peach.optm.linear.__doc__
peach.optm.multivar.__doc__
peach.optm.quasinewton.__doc__
peach.optm.stochastic.__doc__
peach.pso.__doc__
peach.pso.acc.__doc__
peach.pso.base.__doc__
peach.sa.__doc__
peach.sa.base.__doc__
peach.sa.neighbor.__doc__" class="py-name" href="#" onclick="return doclink('link-0', '__doc__', 'link-0');">__doc__</a></tt> <tt class="py-op">=</tt> <tt class="py-docstring">"""</tt> </tt>
<a name="L11"></a><tt class="py-lineno"> 11</tt>  <tt class="py-line"><tt class="py-docstring">This package implements basic multivariable optimizers, including gradient and</tt> </tt>
<a name="L12"></a><tt class="py-lineno"> 12</tt>  <tt class="py-line"><tt class="py-docstring">Newton searches.</tt> </tt>
<a name="L13"></a><tt class="py-lineno"> 13</tt>  <tt class="py-line"><tt class="py-docstring">"""</tt> </tt>
<a name="L14"></a><tt class="py-lineno"> 14</tt>  <tt class="py-line"> </tt>
<a name="L15"></a><tt class="py-lineno"> 15</tt>  <tt class="py-line"><tt class="py-comment">################################################################################</tt> </tt>
<a name="L16"></a><tt class="py-lineno"> 16</tt>  <tt class="py-line"><tt class="py-keyword">from</tt> <tt class="py-name">numpy</tt> <tt class="py-keyword">import</tt> <tt class="py-name">array</tt><tt class="py-op">,</tt> <tt class="py-name">dot</tt><tt class="py-op">,</tt> <tt id="link-1" class="py-name" targets="Variable peach.nn.rbfn.abs=peach.nn.rbfn-module.html#abs,Variable peach.pso.base.abs=peach.pso.base-module.html#abs"><a title="peach.nn.rbfn.abs
peach.pso.base.abs" class="py-name" href="#" onclick="return doclink('link-1', 'abs', 'link-1');">abs</a></tt><tt class="py-op">,</tt> <tt class="py-name">sum</tt><tt class="py-op">,</tt> <tt class="py-name">roll</tt><tt class="py-op">,</tt> <tt class="py-name">ones</tt><tt class="py-op">,</tt> <tt class="py-name">eye</tt><tt class="py-op">,</tt> <tt class="py-name">isscalar</tt><tt class="py-op">,</tt> <tt class="py-name">zeros</tt> </tt>
<a name="L17"></a><tt class="py-lineno"> 17</tt>  <tt class="py-line"><tt class="py-keyword">from</tt> <tt class="py-name">numpy</tt><tt class="py-op">.</tt><tt class="py-name">linalg</tt> <tt class="py-keyword">import</tt> <tt class="py-name">inv</tt> </tt>
<a name="L18"></a><tt class="py-lineno"> 18</tt>  <tt class="py-line"><tt class="py-keyword">from</tt> <tt id="link-2" class="py-name" targets="Module peach.fuzzy.base=peach.fuzzy.base-module.html,Module peach.ga.base=peach.ga.base-module.html,Module peach.nn.base=peach.nn.base-module.html,Module peach.optm.base=peach.optm.base-module.html,Module peach.pso.base=peach.pso.base-module.html,Module peach.sa.base=peach.sa.base-module.html"><a title="peach.fuzzy.base
peach.ga.base
peach.nn.base
peach.optm.base
peach.pso.base
peach.sa.base" class="py-name" href="#" onclick="return doclink('link-2', 'base', 'link-2');">base</a></tt> <tt class="py-keyword">import</tt> <tt id="link-3" class="py-name" targets="Class peach.optm.base.Optimizer=peach.optm.base.Optimizer-class.html"><a title="peach.optm.base.Optimizer" class="py-name" href="#" onclick="return doclink('link-3', 'Optimizer', 'link-3');">Optimizer</a></tt><tt class="py-op">,</tt> <tt id="link-4" class="py-name" targets="Function peach.optm.base.gradient()=peach.optm.base-module.html#gradient"><a title="peach.optm.base.gradient" class="py-name" href="#" onclick="return doclink('link-4', 'gradient', 'link-4');">gradient</a></tt><tt class="py-op">,</tt> <tt id="link-5" class="py-name" targets="Function peach.optm.base.hessian()=peach.optm.base-module.html#hessian"><a title="peach.optm.base.hessian" class="py-name" href="#" onclick="return doclink('link-5', 'hessian', 'link-5');">hessian</a></tt> </tt>
<a name="L19"></a><tt class="py-lineno"> 19</tt>  <tt class="py-line"> </tt>
<a name="L20"></a><tt class="py-lineno"> 20</tt>  <tt class="py-line"> </tt>
<a name="L21"></a><tt class="py-lineno"> 21</tt>  <tt class="py-line"><tt class="py-comment">################################################################################</tt> </tt>
<a name="L22"></a><tt class="py-lineno"> 22</tt>  <tt class="py-line"><tt class="py-comment"># Classes</tt> </tt>
<a name="L23"></a><tt class="py-lineno"> 23</tt>  <tt class="py-line"><tt class="py-comment">################################################################################</tt> </tt>
<a name="Direct"></a><div id="Direct-def"><a name="L24"></a><tt class="py-lineno"> 24</tt> <a class="py-toggle" href="#" id="Direct-toggle" onclick="return toggle('Direct');">-</a><tt class="py-line"><tt class="py-keyword">class</tt> <a class="py-def-name" href="peach.optm.multivar.Direct-class.html">Direct</a><tt class="py-op">(</tt><tt class="py-base-class">Optimizer</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Direct-collapsed" style="display:none;" pad="+++" indent="++++"></div><div id="Direct-expanded"><a name="L25"></a><tt class="py-lineno"> 25</tt>  <tt class="py-line">    <tt class="py-docstring">'''</tt> </tt>
<a name="L26"></a><tt class="py-lineno"> 26</tt>  <tt class="py-line"><tt class="py-docstring">    Multidimensional direct search</tt> </tt>
<a name="L27"></a><tt class="py-lineno"> 27</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L28"></a><tt class="py-lineno"> 28</tt>  <tt class="py-line"><tt class="py-docstring">    This optimization method is a generalization of the 1D method, using</tt> </tt>
<a name="L29"></a><tt class="py-lineno"> 29</tt>  <tt class="py-line"><tt class="py-docstring">    variable swap as search direction. This results in a very simplistic and</tt> </tt>
<a name="L30"></a><tt class="py-lineno"> 30</tt>  <tt class="py-line"><tt class="py-docstring">    inefficient method that should be used only when any other method fails.</tt> </tt>
<a name="L31"></a><tt class="py-lineno"> 31</tt>  <tt class="py-line"><tt class="py-docstring">    '''</tt> </tt>
<a name="Direct.__init__"></a><div id="Direct.__init__-def"><a name="L32"></a><tt class="py-lineno"> 32</tt> <a class="py-toggle" href="#" id="Direct.__init__-toggle" onclick="return toggle('Direct.__init__');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.Direct-class.html#__init__">__init__</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">f</tt><tt class="py-op">,</tt> <tt class="py-param">x0</tt><tt class="py-op">,</tt> <tt class="py-param">ranges</tt><tt class="py-op">=</tt><tt class="py-name">None</tt><tt class="py-op">,</tt> <tt class="py-param">h</tt><tt class="py-op">=</tt><tt class="py-number">0.5</tt><tt class="py-op">,</tt> <tt class="py-param">emax</tt><tt class="py-op">=</tt><tt class="py-number">1e-8</tt><tt class="py-op">,</tt> <tt class="py-param">imax</tt><tt class="py-op">=</tt><tt class="py-number">1000</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Direct.__init__-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="Direct.__init__-expanded"><a name="L33"></a><tt class="py-lineno"> 33</tt>  <tt class="py-line">        <tt class="py-docstring">'''</tt> </tt>
<a name="L34"></a><tt class="py-lineno"> 34</tt>  <tt class="py-line"><tt class="py-docstring">        Initializes the optimizer.</tt> </tt>
<a name="L35"></a><tt class="py-lineno"> 35</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L36"></a><tt class="py-lineno"> 36</tt>  <tt class="py-line"><tt class="py-docstring">        To create an optimizer of this type, instantiate the class with the</tt> </tt>
<a name="L37"></a><tt class="py-lineno"> 37</tt>  <tt class="py-line"><tt class="py-docstring">        parameters given below:</tt> </tt>
<a name="L38"></a><tt class="py-lineno"> 38</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L39"></a><tt class="py-lineno"> 39</tt>  <tt class="py-line"><tt class="py-docstring">        :Parameters:</tt> </tt>
<a name="L40"></a><tt class="py-lineno"> 40</tt>  <tt class="py-line"><tt class="py-docstring">          f</tt> </tt>
<a name="L41"></a><tt class="py-lineno"> 41</tt>  <tt class="py-line"><tt class="py-docstring">            A multivariable function to be optimized. The function should have</tt> </tt>
<a name="L42"></a><tt class="py-lineno"> 42</tt>  <tt class="py-line"><tt class="py-docstring">            only one parameter, a multidimensional line-vector, and return the</tt> </tt>
<a name="L43"></a><tt class="py-lineno"> 43</tt>  <tt class="py-line"><tt class="py-docstring">            function value, a scalar.</tt> </tt>
<a name="L44"></a><tt class="py-lineno"> 44</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L45"></a><tt class="py-lineno"> 45</tt>  <tt class="py-line"><tt class="py-docstring">          x0</tt> </tt>
<a name="L46"></a><tt class="py-lineno"> 46</tt>  <tt class="py-line"><tt class="py-docstring">            First estimate of the minimum. Estimates can be given in any format,</tt> </tt>
<a name="L47"></a><tt class="py-lineno"> 47</tt>  <tt class="py-line"><tt class="py-docstring">            but internally they are converted to a one-dimension vector, where</tt> </tt>
<a name="L48"></a><tt class="py-lineno"> 48</tt>  <tt class="py-line"><tt class="py-docstring">            each component corresponds to the estimate of that particular</tt> </tt>
<a name="L49"></a><tt class="py-lineno"> 49</tt>  <tt class="py-line"><tt class="py-docstring">            variable. The vector is computed by flattening the array.</tt> </tt>
<a name="L50"></a><tt class="py-lineno"> 50</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L51"></a><tt class="py-lineno"> 51</tt>  <tt class="py-line"><tt class="py-docstring">          ranges</tt> </tt>
<a name="L52"></a><tt class="py-lineno"> 52</tt>  <tt class="py-line"><tt class="py-docstring">            A range of values might be passed to the algorithm, but it is not</tt> </tt>
<a name="L53"></a><tt class="py-lineno"> 53</tt>  <tt class="py-line"><tt class="py-docstring">            necessary. If supplied, this parameter should be a list of ranges</tt> </tt>
<a name="L54"></a><tt class="py-lineno"> 54</tt>  <tt class="py-line"><tt class="py-docstring">            for each variable of the objective function. It is specified as a</tt> </tt>
<a name="L55"></a><tt class="py-lineno"> 55</tt>  <tt class="py-line"><tt class="py-docstring">            list of tuples of two values, ``(x0, x1)``, where ``x0`` is the</tt> </tt>
<a name="L56"></a><tt class="py-lineno"> 56</tt>  <tt class="py-line"><tt class="py-docstring">            start of the interval, and ``x1`` its end. Obviously, ``x0`` should</tt> </tt>
<a name="L57"></a><tt class="py-lineno"> 57</tt>  <tt class="py-line"><tt class="py-docstring">            be smaller than ``x1``. It can also be given as a list with a simple</tt> </tt>
<a name="L58"></a><tt class="py-lineno"> 58</tt>  <tt class="py-line"><tt class="py-docstring">            tuple in the same format. In that case, the same range will be</tt> </tt>
<a name="L59"></a><tt class="py-lineno"> 59</tt>  <tt class="py-line"><tt class="py-docstring">            applied for every variable in the optimization.</tt> </tt>
<a name="L60"></a><tt class="py-lineno"> 60</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L61"></a><tt class="py-lineno"> 61</tt>  <tt class="py-line"><tt class="py-docstring">          h</tt> </tt>
<a name="L62"></a><tt class="py-lineno"> 62</tt>  <tt class="py-line"><tt class="py-docstring">            The initial step of the search. Defaults to 0.5</tt> </tt>
<a name="L63"></a><tt class="py-lineno"> 63</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L64"></a><tt class="py-lineno"> 64</tt>  <tt class="py-line"><tt class="py-docstring">          emax</tt> </tt>
<a name="L65"></a><tt class="py-lineno"> 65</tt>  <tt class="py-line"><tt class="py-docstring">            Maximum allowed error. The algorithm stops as soon as the error is</tt> </tt>
<a name="L66"></a><tt class="py-lineno"> 66</tt>  <tt class="py-line"><tt class="py-docstring">            below this level. The error is absolute.</tt> </tt>
<a name="L67"></a><tt class="py-lineno"> 67</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L68"></a><tt class="py-lineno"> 68</tt>  <tt class="py-line"><tt class="py-docstring">          imax</tt> </tt>
<a name="L69"></a><tt class="py-lineno"> 69</tt>  <tt class="py-line"><tt class="py-docstring">            Maximum number of iterations, the algorithm stops as soon this</tt> </tt>
<a name="L70"></a><tt class="py-lineno"> 70</tt>  <tt class="py-line"><tt class="py-docstring">            number of iterations are executed, no matter what the error is at</tt> </tt>
<a name="L71"></a><tt class="py-lineno"> 71</tt>  <tt class="py-line"><tt class="py-docstring">            the moment.</tt> </tt>
<a name="L72"></a><tt class="py-lineno"> 72</tt>  <tt class="py-line"><tt class="py-docstring">        '''</tt> </tt>
<a name="L73"></a><tt class="py-lineno"> 73</tt>  <tt class="py-line">        <tt id="link-6" class="py-name"><a title="peach.optm.base.Optimizer" class="py-name" href="#" onclick="return doclink('link-6', 'Optimizer', 'link-3');">Optimizer</a></tt><tt class="py-op">.</tt><tt id="link-7" class="py-name" targets="Method peach.fuzzy.base.FuzzySet.__init__()=peach.fuzzy.base.FuzzySet-class.html#__init__,Method peach.fuzzy.cmeans.FuzzyCMeans.__init__()=peach.fuzzy.cmeans.FuzzyCMeans-class.html#__init__,Method peach.fuzzy.control.Controller.__init__()=peach.fuzzy.control.Controller-class.html#__init__,Method peach.fuzzy.control.Parametric.__init__()=peach.fuzzy.control.Parametric-class.html#__init__,Method peach.fuzzy.mf.Bell.__init__()=peach.fuzzy.mf.Bell-class.html#__init__,Method peach.fuzzy.mf.DecreasingRamp.__init__()=peach.fuzzy.mf.DecreasingRamp-class.html#__init__,Method peach.fuzzy.mf.DecreasingSigmoid.__init__()=peach.fuzzy.mf.DecreasingSigmoid-class.html#__init__,Method peach.fuzzy.mf.Gaussian.__init__()=peach.fuzzy.mf.Gaussian-class.html#__init__,Method peach.fuzzy.mf.IncreasingRamp.__init__()=peach.fuzzy.mf.IncreasingRamp-class.html#__init__,Method peach.fuzzy.mf.IncreasingSigmoid.__init__()=peach.fuzzy.mf.IncreasingSigmoid-class.html#__init__,Method peach.fuzzy.mf.Membership.__init__()=peach.fuzzy.mf.Membership-class.html#__init__,Method peach.fuzzy.mf.RaisedCosine.__init__()=peach.fuzzy.mf.RaisedCosine-class.html#__init__,Method peach.fuzzy.mf.Smf.__init__()=peach.fuzzy.mf.Smf-class.html#__init__,Method peach.fuzzy.mf.Trapezoid.__init__()=peach.fuzzy.mf.Trapezoid-class.html#__init__,Method peach.fuzzy.mf.Triangle.__init__()=peach.fuzzy.mf.Triangle-class.html#__init__,Method peach.fuzzy.mf.Zmf.__init__()=peach.fuzzy.mf.Zmf-class.html#__init__,Method peach.ga.base.GeneticAlgorithm.__init__()=peach.ga.base.GeneticAlgorithm-class.html#__init__,Method peach.ga.chromosome.Chromosome.__init__()=peach.ga.chromosome.Chromosome-class.html#__init__,Method peach.ga.crossover.OnePoint.__init__()=peach.ga.crossover.OnePoint-class.html#__init__,Method peach.ga.crossover.TwoPoint.__init__()=peach.ga.crossover.TwoPoint-class.html#__init__,Method peach.ga.crossover.Uniform.__init__()=peach.ga.crossover.Uniform-class.html#__init__,Method peach.ga.fitness.Fitness.__init__()=peach.ga.fitness.Fitness-class.html#__init__,Method peach.ga.fitness.Ranking.__init__()=peach.ga.fitness.Ranking-class.html#__init__,Method peach.ga.mutation.BitToBit.__init__()=peach.ga.mutation.BitToBit-class.html#__init__,Method peach.nn.af.Activation.__init__()=peach.nn.af.Activation-class.html#__init__,Method peach.nn.af.ArcTan.__init__()=peach.nn.af.ArcTan-class.html#__init__,Method peach.nn.af.Gaussian.__init__()=peach.nn.af.Gaussian-class.html#__init__,Method peach.nn.af.Linear.__init__()=peach.nn.af.Linear-class.html#__init__,Method peach.nn.af.Ramp.__init__()=peach.nn.af.Ramp-class.html#__init__,Method peach.nn.af.Sigmoid.__init__()=peach.nn.af.Sigmoid-class.html#__init__,Method peach.nn.af.Signum.__init__()=peach.nn.af.Signum-class.html#__init__,Method peach.nn.af.TanH.__init__()=peach.nn.af.TanH-class.html#__init__,Method peach.nn.af.Threshold.__init__()=peach.nn.af.Threshold-class.html#__init__,Method peach.nn.base.Layer.__init__()=peach.nn.base.Layer-class.html#__init__,Method peach.nn.kmeans.KMeans.__init__()=peach.nn.kmeans.KMeans-class.html#__init__,Method peach.nn.lrules.BackPropagation.__init__()=peach.nn.lrules.BackPropagation-class.html#__init__,Method peach.nn.lrules.Competitive.__init__()=peach.nn.lrules.Competitive-class.html#__init__,Method peach.nn.lrules.Cooperative.__init__()=peach.nn.lrules.Cooperative-class.html#__init__,Method peach.nn.lrules.LMS.__init__()=peach.nn.lrules.LMS-class.html#__init__,Method peach.nn.lrules.WinnerTakesAll.__init__()=peach.nn.lrules.WinnerTakesAll-class.html#__init__,Method peach.nn.mem.Hopfield.__init__()=peach.nn.mem.Hopfield-class.html#__init__,Method peach.nn.nnet.FeedForward.__init__()=peach.nn.nnet.FeedForward-class.html#__init__,Method peach.nn.nnet.GRNN.__init__()=peach.nn.nnet.GRNN-class.html#__init__,Method peach.nn.nnet.PNN.__init__()=peach.nn.nnet.PNN-class.html#__init__,Method peach.nn.nnet.SOM.__init__()=peach.nn.nnet.SOM-class.html#__init__,Method peach.nn.rbfn.RBFN.__init__()=peach.nn.rbfn.RBFN-class.html#__init__,Method peach.optm.base.Optimizer.__init__()=peach.optm.base.Optimizer-class.html#__init__,Method peach.optm.linear.Direct1D.__init__()=peach.optm.linear.Direct1D-class.html#__init__,Method peach.optm.linear.Fibonacci.__init__()=peach.optm.linear.Fibonacci-class.html#__init__,Method peach.optm.linear.GoldenRule.__init__()=peach.optm.linear.GoldenRule-class.html#__init__,Method peach.optm.linear.Interpolation.__init__()=peach.optm.linear.Interpolation-class.html#__init__,Method peach.optm.multivar.Direct.__init__()=peach.optm.multivar.Direct-class.html#__init__,Method peach.optm.multivar.Gradient.__init__()=peach.optm.multivar.Gradient-class.html#__init__,Method peach.optm.multivar.MomentumGradient.__init__()=peach.optm.multivar.MomentumGradient-class.html#__init__,Method peach.optm.multivar.Newton.__init__()=peach.optm.multivar.Newton-class.html#__init__,Method peach.optm.quasinewton.BFGS.__init__()=peach.optm.quasinewton.BFGS-class.html#__init__,Method peach.optm.quasinewton.DFP.__init__()=peach.optm.quasinewton.DFP-class.html#__init__,Method peach.optm.quasinewton.SR1.__init__()=peach.optm.quasinewton.SR1-class.html#__init__,Method peach.optm.stochastic.CrossEntropy.__init__()=peach.optm.stochastic.CrossEntropy-class.html#__init__,Method peach.pso.acc.Accelerator.__init__()=peach.pso.acc.Accelerator-class.html#__init__,Method peach.pso.acc.StandardPSO.__init__()=peach.pso.acc.StandardPSO-class.html#__init__,Method peach.pso.base.ParticleSwarmOptimizer.__init__()=peach.pso.base.ParticleSwarmOptimizer-class.html#__init__,Method peach.sa.base.BinarySA.__init__()=peach.sa.base.BinarySA-class.html#__init__,Method peach.sa.base.ContinuousSA.__init__()=peach.sa.base.ContinuousSA-class.html#__init__,Method peach.sa.neighbor.BinaryNeighbor.__init__()=peach.sa.neighbor.BinaryNeighbor-class.html#__init__,Method peach.sa.neighbor.ContinuousNeighbor.__init__()=peach.sa.neighbor.ContinuousNeighbor-class.html#__init__,Method peach.sa.neighbor.GaussianNeighbor.__init__()=peach.sa.neighbor.GaussianNeighbor-class.html#__init__,Method peach.sa.neighbor.InvertBitsNeighbor.__init__()=peach.sa.neighbor.InvertBitsNeighbor-class.html#__init__,Method peach.sa.neighbor.UniformNeighbor.__init__()=peach.sa.neighbor.UniformNeighbor-class.html#__init__"><a title="peach.fuzzy.base.FuzzySet.__init__
peach.fuzzy.cmeans.FuzzyCMeans.__init__
peach.fuzzy.control.Controller.__init__
peach.fuzzy.control.Parametric.__init__
peach.fuzzy.mf.Bell.__init__
peach.fuzzy.mf.DecreasingRamp.__init__
peach.fuzzy.mf.DecreasingSigmoid.__init__
peach.fuzzy.mf.Gaussian.__init__
peach.fuzzy.mf.IncreasingRamp.__init__
peach.fuzzy.mf.IncreasingSigmoid.__init__
peach.fuzzy.mf.Membership.__init__
peach.fuzzy.mf.RaisedCosine.__init__
peach.fuzzy.mf.Smf.__init__
peach.fuzzy.mf.Trapezoid.__init__
peach.fuzzy.mf.Triangle.__init__
peach.fuzzy.mf.Zmf.__init__
peach.ga.base.GeneticAlgorithm.__init__
peach.ga.chromosome.Chromosome.__init__
peach.ga.crossover.OnePoint.__init__
peach.ga.crossover.TwoPoint.__init__
peach.ga.crossover.Uniform.__init__
peach.ga.fitness.Fitness.__init__
peach.ga.fitness.Ranking.__init__
peach.ga.mutation.BitToBit.__init__
peach.nn.af.Activation.__init__
peach.nn.af.ArcTan.__init__
peach.nn.af.Gaussian.__init__
peach.nn.af.Linear.__init__
peach.nn.af.Ramp.__init__
peach.nn.af.Sigmoid.__init__
peach.nn.af.Signum.__init__
peach.nn.af.TanH.__init__
peach.nn.af.Threshold.__init__
peach.nn.base.Layer.__init__
peach.nn.kmeans.KMeans.__init__
peach.nn.lrules.BackPropagation.__init__
peach.nn.lrules.Competitive.__init__
peach.nn.lrules.Cooperative.__init__
peach.nn.lrules.LMS.__init__
peach.nn.lrules.WinnerTakesAll.__init__
peach.nn.mem.Hopfield.__init__
peach.nn.nnet.FeedForward.__init__
peach.nn.nnet.GRNN.__init__
peach.nn.nnet.PNN.__init__
peach.nn.nnet.SOM.__init__
peach.nn.rbfn.RBFN.__init__
peach.optm.base.Optimizer.__init__
peach.optm.linear.Direct1D.__init__
peach.optm.linear.Fibonacci.__init__
peach.optm.linear.GoldenRule.__init__
peach.optm.linear.Interpolation.__init__
peach.optm.multivar.Direct.__init__
peach.optm.multivar.Gradient.__init__
peach.optm.multivar.MomentumGradient.__init__
peach.optm.multivar.Newton.__init__
peach.optm.quasinewton.BFGS.__init__
peach.optm.quasinewton.DFP.__init__
peach.optm.quasinewton.SR1.__init__
peach.optm.stochastic.CrossEntropy.__init__
peach.pso.acc.Accelerator.__init__
peach.pso.acc.StandardPSO.__init__
peach.pso.base.ParticleSwarmOptimizer.__init__
peach.sa.base.BinarySA.__init__
peach.sa.base.ContinuousSA.__init__
peach.sa.neighbor.BinaryNeighbor.__init__
peach.sa.neighbor.ContinuousNeighbor.__init__
peach.sa.neighbor.GaussianNeighbor.__init__
peach.sa.neighbor.InvertBitsNeighbor.__init__
peach.sa.neighbor.UniformNeighbor.__init__" class="py-name" href="#" onclick="return doclink('link-7', '__init__', 'link-7');">__init__</a></tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">)</tt> </tt>
<a name="L74"></a><tt class="py-lineno"> 74</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__f</tt> <tt class="py-op">=</tt> <tt class="py-name">f</tt> </tt>
<a name="L75"></a><tt class="py-lineno"> 75</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt> <tt class="py-op">=</tt> <tt class="py-name">array</tt><tt class="py-op">(</tt><tt class="py-name">x0</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt class="py-name">ravel</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L76"></a><tt class="py-lineno"> 76</tt>  <tt class="py-line">        <tt class="py-name">n</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt><tt class="py-op">.</tt><tt id="link-8" class="py-name" targets="Variable peach.ga.chromosome.Chromosome.size=peach.ga.chromosome.Chromosome-class.html#size,Variable peach.nn.base.Layer.size=peach.nn.base.Layer-class.html#size"><a title="peach.ga.chromosome.Chromosome.size
peach.nn.base.Layer.size" class="py-name" href="#" onclick="return doclink('link-8', 'size', 'link-8');">size</a></tt> </tt>
<a name="L77"></a><tt class="py-lineno"> 77</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__h</tt> <tt class="py-op">=</tt> <tt class="py-name">ones</tt><tt class="py-op">(</tt><tt class="py-op">(</tt><tt class="py-name">n</tt><tt class="py-op">,</tt> <tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
<a name="L78"></a><tt class="py-lineno"> 78</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__h</tt><tt class="py-op">[</tt><tt class="py-number">0</tt><tt class="py-op">]</tt> <tt class="py-op">=</tt> <tt class="py-op">-</tt><tt class="py-number">0.5</tt> </tt>
<a name="L79"></a><tt class="py-lineno"> 79</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__dx</tt> <tt class="py-op">=</tt> <tt class="py-name">h</tt> <tt class="py-op">*</tt> <tt class="py-name">eye</tt><tt class="py-op">(</tt><tt class="py-name">n</tt><tt class="py-op">,</tt> <tt class="py-number">1</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt class="py-name">reshape</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt><tt class="py-op">.</tt><tt id="link-9" class="py-name" targets="Variable peach.nn.base.Layer.shape=peach.nn.base.Layer-class.html#shape"><a title="peach.nn.base.Layer.shape" class="py-name" href="#" onclick="return doclink('link-9', 'shape', 'link-9');">shape</a></tt><tt class="py-op">)</tt> </tt>
<a name="L80"></a><tt class="py-lineno"> 80</tt>  <tt class="py-line"> </tt>
<a name="L81"></a><tt class="py-lineno"> 81</tt>  <tt class="py-line">        <tt class="py-comment"># Determine ranges of the variables</tt> </tt>
<a name="L82"></a><tt class="py-lineno"> 82</tt>  <tt class="py-line">        <tt class="py-keyword">if</tt> <tt class="py-name">ranges</tt> <tt class="py-keyword">is</tt> <tt class="py-keyword">not</tt> <tt class="py-name">None</tt><tt class="py-op">:</tt> </tt>
<a name="L83"></a><tt class="py-lineno"> 83</tt>  <tt class="py-line">            <tt class="py-name">ranges</tt> <tt class="py-op">=</tt> <tt class="py-name">list</tt><tt class="py-op">(</tt><tt class="py-name">ranges</tt><tt class="py-op">)</tt> </tt>
<a name="L84"></a><tt class="py-lineno"> 84</tt>  <tt class="py-line">            <tt class="py-keyword">if</tt> <tt class="py-name">len</tt><tt class="py-op">(</tt><tt class="py-name">ranges</tt><tt class="py-op">)</tt> <tt class="py-op">==</tt> <tt class="py-number">1</tt><tt class="py-op">:</tt> </tt>
<a name="L85"></a><tt class="py-lineno"> 85</tt>  <tt class="py-line">                <tt class="py-name">ranges</tt> <tt class="py-op">=</tt> <tt class="py-name">array</tt><tt class="py-op">(</tt><tt class="py-name">ranges</tt> <tt class="py-op">*</tt> <tt class="py-name">len</tt><tt class="py-op">(</tt><tt class="py-name">x0</tt><tt class="py-op">[</tt><tt class="py-number">0</tt><tt class="py-op">]</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
<a name="L86"></a><tt class="py-lineno"> 86</tt>  <tt class="py-line">            <tt class="py-keyword">else</tt><tt class="py-op">:</tt> </tt>
<a name="L87"></a><tt class="py-lineno"> 87</tt>  <tt class="py-line">                <tt class="py-name">ranges</tt> <tt class="py-op">=</tt> <tt class="py-name">array</tt><tt class="py-op">(</tt><tt class="py-name">ranges</tt><tt class="py-op">)</tt> </tt>
<a name="L88"></a><tt class="py-lineno"> 88</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">ranges</tt> <tt class="py-op">=</tt> <tt class="py-name">ranges</tt> </tt>
<a name="L89"></a><tt class="py-lineno"> 89</tt>  <tt class="py-line">        <tt class="py-string">'''Holds the ranges for every variable. Although it is a writable</tt> </tt>
<a name="L90"></a><tt class="py-lineno"> 90</tt>  <tt class="py-line"><tt class="py-string">        property, care should be taken in changing parameters before ending the</tt> </tt>
<a name="L91"></a><tt class="py-lineno"> 91</tt>  <tt class="py-line"><tt class="py-string">        convergence.'''</tt> </tt>
<a name="L92"></a><tt class="py-lineno"> 92</tt>  <tt class="py-line"> </tt>
<a name="L93"></a><tt class="py-lineno"> 93</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__emax</tt> <tt class="py-op">=</tt> <tt class="py-name">float</tt><tt class="py-op">(</tt><tt class="py-name">emax</tt><tt class="py-op">)</tt> </tt>
<a name="L94"></a><tt class="py-lineno"> 94</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__imax</tt> <tt class="py-op">=</tt> <tt class="py-name">int</tt><tt class="py-op">(</tt><tt class="py-name">imax</tt><tt class="py-op">)</tt> </tt>
</div><a name="L95"></a><tt class="py-lineno"> 95</tt>  <tt class="py-line"> </tt>
<a name="L96"></a><tt class="py-lineno"> 96</tt>  <tt class="py-line"> </tt>
<a name="Direct.__get_x"></a><div id="Direct.__get_x-def"><a name="L97"></a><tt class="py-lineno"> 97</tt> <a class="py-toggle" href="#" id="Direct.__get_x-toggle" onclick="return toggle('Direct.__get_x');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.Direct-class.html#__get_x">__get_x</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Direct.__get_x-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="Direct.__get_x-expanded"><a name="L98"></a><tt class="py-lineno"> 98</tt>  <tt class="py-line">        <tt class="py-keyword">return</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt> </tt>
</div><a name="L99"></a><tt class="py-lineno"> 99</tt>  <tt class="py-line"> </tt>
<a name="Direct.__set_x"></a><div id="Direct.__set_x-def"><a name="L100"></a><tt class="py-lineno">100</tt> <a class="py-toggle" href="#" id="Direct.__set_x-toggle" onclick="return toggle('Direct.__set_x');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.Direct-class.html#__set_x">__set_x</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">x0</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Direct.__set_x-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="Direct.__set_x-expanded"><a name="L101"></a><tt class="py-lineno">101</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt id="link-10" class="py-name" targets="Method peach.ga.base.GeneticAlgorithm.restart()=peach.ga.base.GeneticAlgorithm-class.html#restart,Method peach.optm.linear.Direct1D.restart()=peach.optm.linear.Direct1D-class.html#restart,Method peach.optm.linear.Fibonacci.restart()=peach.optm.linear.Fibonacci-class.html#restart,Method peach.optm.linear.GoldenRule.restart()=peach.optm.linear.GoldenRule-class.html#restart,Method peach.optm.linear.Interpolation.restart()=peach.optm.linear.Interpolation-class.html#restart,Method peach.optm.multivar.Direct.restart()=peach.optm.multivar.Direct-class.html#restart,Method peach.optm.multivar.Gradient.restart()=peach.optm.multivar.Gradient-class.html#restart,Method peach.optm.multivar.MomentumGradient.restart()=peach.optm.multivar.MomentumGradient-class.html#restart,Method peach.optm.multivar.Newton.restart()=peach.optm.multivar.Newton-class.html#restart,Method peach.optm.quasinewton.BFGS.restart()=peach.optm.quasinewton.BFGS-class.html#restart,Method peach.optm.quasinewton.DFP.restart()=peach.optm.quasinewton.DFP-class.html#restart,Method peach.optm.quasinewton.SR1.restart()=peach.optm.quasinewton.SR1-class.html#restart,Method peach.pso.base.ParticleSwarmOptimizer.restart()=peach.pso.base.ParticleSwarmOptimizer-class.html#restart,Method peach.sa.base.BinarySA.restart()=peach.sa.base.BinarySA-class.html#restart,Method peach.sa.base.ContinuousSA.restart()=peach.sa.base.ContinuousSA-class.html#restart"><a title="peach.ga.base.GeneticAlgorithm.restart
peach.optm.linear.Direct1D.restart
peach.optm.linear.Fibonacci.restart
peach.optm.linear.GoldenRule.restart
peach.optm.linear.Interpolation.restart
peach.optm.multivar.Direct.restart
peach.optm.multivar.Gradient.restart
peach.optm.multivar.MomentumGradient.restart
peach.optm.multivar.Newton.restart
peach.optm.quasinewton.BFGS.restart
peach.optm.quasinewton.DFP.restart
peach.optm.quasinewton.SR1.restart
peach.pso.base.ParticleSwarmOptimizer.restart
peach.sa.base.BinarySA.restart
peach.sa.base.ContinuousSA.restart" class="py-name" href="#" onclick="return doclink('link-10', 'restart', 'link-10');">restart</a></tt><tt class="py-op">(</tt><tt class="py-name">x0</tt><tt class="py-op">)</tt> </tt>
</div><a name="L102"></a><tt class="py-lineno">102</tt>  <tt class="py-line"> </tt>
<a name="L103"></a><tt class="py-lineno">103</tt>  <tt class="py-line">    <tt id="link-11" class="py-name" targets="Variable peach.fuzzy.cmeans.FuzzyCMeans.x=peach.fuzzy.cmeans.FuzzyCMeans-class.html#x,Variable peach.optm.linear.Direct1D.x=peach.optm.linear.Direct1D-class.html#x,Variable peach.optm.linear.GoldenRule.x=peach.optm.linear.GoldenRule-class.html#x,Variable peach.optm.linear.Interpolation.x=peach.optm.linear.Interpolation-class.html#x,Variable peach.optm.multivar.Direct.x=peach.optm.multivar.Direct-class.html#x,Variable peach.optm.multivar.Gradient.x=peach.optm.multivar.Gradient-class.html#x,Variable peach.optm.multivar.MomentumGradient.x=peach.optm.multivar.MomentumGradient-class.html#x,Variable peach.optm.multivar.Newton.x=peach.optm.multivar.Newton-class.html#x,Variable peach.optm.quasinewton.DFP.x=peach.optm.quasinewton.DFP-class.html#x,Variable peach.optm.quasinewton.SR1.x=peach.optm.quasinewton.SR1-class.html#x,Variable peach.sa.base.BinarySA.x=peach.sa.base.BinarySA-class.html#x,Variable peach.sa.base.ContinuousSA.x=peach.sa.base.ContinuousSA-class.html#x"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-11', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt class="py-name">property</tt><tt class="py-op">(</tt><tt id="link-12" class="py-name" targets="Method peach.optm.linear.Direct1D.__get_x()=peach.optm.linear.Direct1D-class.html#__get_x,Method peach.optm.linear.GoldenRule.__get_x()=peach.optm.linear.GoldenRule-class.html#__get_x,Method peach.optm.linear.Interpolation.__get_x()=peach.optm.linear.Interpolation-class.html#__get_x,Method peach.optm.multivar.Direct.__get_x()=peach.optm.multivar.Direct-class.html#__get_x,Method peach.optm.multivar.Gradient.__get_x()=peach.optm.multivar.Gradient-class.html#__get_x,Method peach.optm.multivar.MomentumGradient.__get_x()=peach.optm.multivar.MomentumGradient-class.html#__get_x,Method peach.optm.multivar.Newton.__get_x()=peach.optm.multivar.Newton-class.html#__get_x,Method peach.optm.quasinewton.DFP.__get_x()=peach.optm.quasinewton.DFP-class.html#__get_x,Method peach.optm.quasinewton.SR1.__get_x()=peach.optm.quasinewton.SR1-class.html#__get_x,Method peach.sa.base.BinarySA.__get_x()=peach.sa.base.BinarySA-class.html#__get_x,Method peach.sa.base.ContinuousSA.__get_x()=peach.sa.base.ContinuousSA-class.html#__get_x"><a title="peach.optm.linear.Direct1D.__get_x
peach.optm.linear.GoldenRule.__get_x
peach.optm.linear.Interpolation.__get_x
peach.optm.multivar.Direct.__get_x
peach.optm.multivar.Gradient.__get_x
peach.optm.multivar.MomentumGradient.__get_x
peach.optm.multivar.Newton.__get_x
peach.optm.quasinewton.DFP.__get_x
peach.optm.quasinewton.SR1.__get_x
peach.sa.base.BinarySA.__get_x
peach.sa.base.ContinuousSA.__get_x" class="py-name" href="#" onclick="return doclink('link-12', '__get_x', 'link-12');">__get_x</a></tt><tt class="py-op">,</tt> <tt id="link-13" class="py-name" targets="Method peach.optm.linear.Direct1D.__set_x()=peach.optm.linear.Direct1D-class.html#__set_x,Method peach.optm.linear.GoldenRule.__set_x()=peach.optm.linear.GoldenRule-class.html#__set_x,Method peach.optm.linear.Interpolation.__set_x()=peach.optm.linear.Interpolation-class.html#__set_x,Method peach.optm.multivar.Direct.__set_x()=peach.optm.multivar.Direct-class.html#__set_x,Method peach.optm.multivar.Gradient.__set_x()=peach.optm.multivar.Gradient-class.html#__set_x,Method peach.optm.multivar.MomentumGradient.__set_x()=peach.optm.multivar.MomentumGradient-class.html#__set_x,Method peach.optm.multivar.Newton.__set_x()=peach.optm.multivar.Newton-class.html#__set_x,Method peach.optm.quasinewton.DFP.__set_x()=peach.optm.quasinewton.DFP-class.html#__set_x,Method peach.optm.quasinewton.SR1.__set_x()=peach.optm.quasinewton.SR1-class.html#__set_x,Method peach.sa.base.BinarySA.__set_x()=peach.sa.base.BinarySA-class.html#__set_x,Method peach.sa.base.ContinuousSA.__set_x()=peach.sa.base.ContinuousSA-class.html#__set_x"><a title="peach.optm.linear.Direct1D.__set_x
peach.optm.linear.GoldenRule.__set_x
peach.optm.linear.Interpolation.__set_x
peach.optm.multivar.Direct.__set_x
peach.optm.multivar.Gradient.__set_x
peach.optm.multivar.MomentumGradient.__set_x
peach.optm.multivar.Newton.__set_x
peach.optm.quasinewton.DFP.__set_x
peach.optm.quasinewton.SR1.__set_x
peach.sa.base.BinarySA.__set_x
peach.sa.base.ContinuousSA.__set_x" class="py-name" href="#" onclick="return doclink('link-13', '__set_x', 'link-13');">__set_x</a></tt><tt class="py-op">)</tt> </tt>
<a name="L104"></a><tt class="py-lineno">104</tt>  <tt class="py-line">    <tt class="py-string">'''The estimate of the position of the minimum.'''</tt> </tt>
<a name="L105"></a><tt class="py-lineno">105</tt>  <tt class="py-line"> </tt>
<a name="L106"></a><tt class="py-lineno">106</tt>  <tt class="py-line"> </tt>
<a name="Direct.restart"></a><div id="Direct.restart-def"><a name="L107"></a><tt class="py-lineno">107</tt> <a class="py-toggle" href="#" id="Direct.restart-toggle" onclick="return toggle('Direct.restart');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.Direct-class.html#restart">restart</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">x0</tt><tt class="py-op">,</tt> <tt class="py-param">h</tt><tt class="py-op">=</tt><tt class="py-number">0.5</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Direct.restart-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="Direct.restart-expanded"><a name="L108"></a><tt class="py-lineno">108</tt>  <tt class="py-line">        <tt class="py-docstring">'''</tt> </tt>
<a name="L109"></a><tt class="py-lineno">109</tt>  <tt class="py-line"><tt class="py-docstring">        Resets the optimizer, returning to its original state, and allowing to</tt> </tt>
<a name="L110"></a><tt class="py-lineno">110</tt>  <tt class="py-line"><tt class="py-docstring">        use a new first estimate.</tt> </tt>
<a name="L111"></a><tt class="py-lineno">111</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L112"></a><tt class="py-lineno">112</tt>  <tt class="py-line"><tt class="py-docstring">        :Parameters:</tt> </tt>
<a name="L113"></a><tt class="py-lineno">113</tt>  <tt class="py-line"><tt class="py-docstring">          x0</tt> </tt>
<a name="L114"></a><tt class="py-lineno">114</tt>  <tt class="py-line"><tt class="py-docstring">            New estimate of the minimum. Estimates can be given in any format,</tt> </tt>
<a name="L115"></a><tt class="py-lineno">115</tt>  <tt class="py-line"><tt class="py-docstring">            but internally they are converted to a one-dimension vector, where</tt> </tt>
<a name="L116"></a><tt class="py-lineno">116</tt>  <tt class="py-line"><tt class="py-docstring">            each component corresponds to the estimate of that particular</tt> </tt>
<a name="L117"></a><tt class="py-lineno">117</tt>  <tt class="py-line"><tt class="py-docstring">            variable. The vector is computed by flattening the array.</tt> </tt>
<a name="L118"></a><tt class="py-lineno">118</tt>  <tt class="py-line"><tt class="py-docstring">          h</tt> </tt>
<a name="L119"></a><tt class="py-lineno">119</tt>  <tt class="py-line"><tt class="py-docstring">            The initial step of the search. Defaults to 0.5</tt> </tt>
<a name="L120"></a><tt class="py-lineno">120</tt>  <tt class="py-line"><tt class="py-docstring">        '''</tt> </tt>
<a name="L121"></a><tt class="py-lineno">121</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt> <tt class="py-op">=</tt> <tt class="py-name">array</tt><tt class="py-op">(</tt><tt class="py-name">x0</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt class="py-name">ravel</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L122"></a><tt class="py-lineno">122</tt>  <tt class="py-line">        <tt class="py-name">n</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt><tt class="py-op">.</tt><tt id="link-14" class="py-name"><a title="peach.ga.chromosome.Chromosome.size
peach.nn.base.Layer.size" class="py-name" href="#" onclick="return doclink('link-14', 'size', 'link-8');">size</a></tt> </tt>
<a name="L123"></a><tt class="py-lineno">123</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__h</tt> <tt class="py-op">=</tt> <tt class="py-name">ones</tt><tt class="py-op">(</tt><tt class="py-op">(</tt><tt class="py-name">n</tt><tt class="py-op">,</tt> <tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
<a name="L124"></a><tt class="py-lineno">124</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__h</tt><tt class="py-op">[</tt><tt class="py-number">0</tt><tt class="py-op">]</tt> <tt class="py-op">=</tt> <tt class="py-op">-</tt><tt class="py-number">0.5</tt> </tt>
<a name="L125"></a><tt class="py-lineno">125</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__dx</tt> <tt class="py-op">=</tt> <tt class="py-name">h</tt> <tt class="py-op">*</tt> <tt class="py-name">eye</tt><tt class="py-op">(</tt><tt class="py-name">n</tt><tt class="py-op">,</tt> <tt class="py-number">1</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt class="py-name">reshape</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt><tt class="py-op">.</tt><tt id="link-15" class="py-name"><a title="peach.nn.base.Layer.shape" class="py-name" href="#" onclick="return doclink('link-15', 'shape', 'link-9');">shape</a></tt><tt class="py-op">)</tt> </tt>
</div><a name="L126"></a><tt class="py-lineno">126</tt>  <tt class="py-line"> </tt>
<a name="L127"></a><tt class="py-lineno">127</tt>  <tt class="py-line"> </tt>
<a name="Direct.step"></a><div id="Direct.step-def"><a name="L128"></a><tt class="py-lineno">128</tt> <a class="py-toggle" href="#" id="Direct.step-toggle" onclick="return toggle('Direct.step');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.Direct-class.html#step">step</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Direct.step-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="Direct.step-expanded"><a name="L129"></a><tt class="py-lineno">129</tt>  <tt class="py-line">        <tt class="py-docstring">'''</tt> </tt>
<a name="L130"></a><tt class="py-lineno">130</tt>  <tt class="py-line"><tt class="py-docstring">        One step of the search.</tt> </tt>
<a name="L131"></a><tt class="py-lineno">131</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L132"></a><tt class="py-lineno">132</tt>  <tt class="py-line"><tt class="py-docstring">        In this method, the result of the step is highly dependent of the steps</tt> </tt>
<a name="L133"></a><tt class="py-lineno">133</tt>  <tt class="py-line"><tt class="py-docstring">        executed before, as the search step is updated at each call to this</tt> </tt>
<a name="L134"></a><tt class="py-lineno">134</tt>  <tt class="py-line"><tt class="py-docstring">        method.</tt> </tt>
<a name="L135"></a><tt class="py-lineno">135</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L136"></a><tt class="py-lineno">136</tt>  <tt class="py-line"><tt class="py-docstring">        :Returns:</tt> </tt>
<a name="L137"></a><tt class="py-lineno">137</tt>  <tt class="py-line"><tt class="py-docstring">          This method returns a tuple ``(x, e)``, where ``x`` is the updated</tt> </tt>
<a name="L138"></a><tt class="py-lineno">138</tt>  <tt class="py-line"><tt class="py-docstring">          estimate of the minimum, and ``e`` is the estimated error.</tt> </tt>
<a name="L139"></a><tt class="py-lineno">139</tt>  <tt class="py-line"><tt class="py-docstring">        '''</tt> </tt>
<a name="L140"></a><tt class="py-lineno">140</tt>  <tt class="py-line">        <tt class="py-name">f</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__f</tt> </tt>
<a name="L141"></a><tt class="py-lineno">141</tt>  <tt class="py-line">        <tt id="link-16" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-16', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt> </tt>
<a name="L142"></a><tt class="py-lineno">142</tt>  <tt class="py-line">        <tt class="py-name">dx</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__dx</tt> </tt>
<a name="L143"></a><tt class="py-lineno">143</tt>  <tt class="py-line">        <tt class="py-name">fo</tt> <tt class="py-op">=</tt> <tt class="py-name">f</tt><tt class="py-op">(</tt><tt id="link-17" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-17', 'x', 'link-11');">x</a></tt><tt class="py-op">)</tt> </tt>
<a name="L144"></a><tt class="py-lineno">144</tt>  <tt class="py-line"> </tt>
<a name="L145"></a><tt class="py-lineno">145</tt>  <tt class="py-line">        <tt class="py-comment"># Next estimate</tt> </tt>
<a name="L146"></a><tt class="py-lineno">146</tt>  <tt class="py-line">        <tt id="link-18" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-18', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt id="link-19" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-19', 'x', 'link-11');">x</a></tt> <tt class="py-op">+</tt> <tt class="py-name">dx</tt> </tt>
<a name="L147"></a><tt class="py-lineno">147</tt>  <tt class="py-line"> </tt>
<a name="L148"></a><tt class="py-lineno">148</tt>  <tt class="py-line">        <tt class="py-comment"># Sanity check</tt> </tt>
<a name="L149"></a><tt class="py-lineno">149</tt>  <tt class="py-line">        <tt class="py-keyword">if</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">ranges</tt> <tt class="py-keyword">is</tt> <tt class="py-keyword">not</tt> <tt class="py-name">None</tt><tt class="py-op">:</tt> </tt>
<a name="L150"></a><tt class="py-lineno">150</tt>  <tt class="py-line">            <tt class="py-name">r0</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">ranges</tt><tt class="py-op">[</tt><tt class="py-op">:</tt><tt class="py-op">,</tt> <tt class="py-number">0</tt><tt class="py-op">]</tt> </tt>
<a name="L151"></a><tt class="py-lineno">151</tt>  <tt class="py-line">            <tt class="py-name">r1</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">ranges</tt><tt class="py-op">[</tt><tt class="py-op">:</tt><tt class="py-op">,</tt> <tt class="py-number">1</tt><tt class="py-op">]</tt> </tt>
<a name="L152"></a><tt class="py-lineno">152</tt>  <tt class="py-line">            <tt id="link-20" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-20', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt class="py-name">where</tt><tt class="py-op">(</tt><tt id="link-21" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-21', 'x', 'link-11');">x</a></tt> <tt class="py-op">&lt;</tt> <tt class="py-name">r0</tt><tt class="py-op">,</tt> <tt class="py-name">r0</tt><tt class="py-op">,</tt> <tt id="link-22" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-22', 'x', 'link-11');">x</a></tt><tt class="py-op">)</tt> </tt>
<a name="L153"></a><tt class="py-lineno">153</tt>  <tt class="py-line">            <tt id="link-23" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-23', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt class="py-name">where</tt><tt class="py-op">(</tt><tt id="link-24" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-24', 'x', 'link-11');">x</a></tt> <tt class="py-op">&gt;</tt> <tt class="py-name">r1</tt><tt class="py-op">,</tt> <tt class="py-name">r1</tt><tt class="py-op">,</tt> <tt id="link-25" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-25', 'x', 'link-11');">x</a></tt><tt class="py-op">)</tt> </tt>
<a name="L154"></a><tt class="py-lineno">154</tt>  <tt class="py-line"> </tt>
<a name="L155"></a><tt class="py-lineno">155</tt>  <tt class="py-line">        <tt class="py-comment"># Update state</tt> </tt>
<a name="L156"></a><tt class="py-lineno">156</tt>  <tt class="py-line">        <tt class="py-name">fn</tt> <tt class="py-op">=</tt> <tt class="py-name">f</tt><tt class="py-op">(</tt><tt id="link-26" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-26', 'x', 'link-11');">x</a></tt><tt class="py-op">)</tt> </tt>
<a name="L157"></a><tt class="py-lineno">157</tt>  <tt class="py-line">        <tt class="py-keyword">if</tt> <tt class="py-name">fn</tt> <tt class="py-op">&gt;</tt> <tt class="py-name">fo</tt><tt class="py-op">:</tt> </tt>
<a name="L158"></a><tt class="py-lineno">158</tt>  <tt class="py-line">            <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__dx</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__h</tt> <tt class="py-op">*</tt> <tt class="py-name">roll</tt><tt class="py-op">(</tt><tt class="py-name">dx</tt><tt class="py-op">,</tt> <tt class="py-number">1</tt><tt class="py-op">)</tt> </tt>
<a name="L159"></a><tt class="py-lineno">159</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt> <tt class="py-op">=</tt> <tt id="link-27" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-27', 'x', 'link-11');">x</a></tt> </tt>
<a name="L160"></a><tt class="py-lineno">160</tt>  <tt class="py-line">        <tt class="py-keyword">return</tt> <tt id="link-28" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-28', 'x', 'link-11');">x</a></tt><tt class="py-op">,</tt> <tt class="py-name">sum</tt><tt class="py-op">(</tt><tt id="link-29" class="py-name"><a title="peach.nn.rbfn.abs
peach.pso.base.abs" class="py-name" href="#" onclick="return doclink('link-29', 'abs', 'link-1');">abs</a></tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__dx</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
</div><a name="L161"></a><tt class="py-lineno">161</tt>  <tt class="py-line"> </tt>
<a name="L162"></a><tt class="py-lineno">162</tt>  <tt class="py-line"> </tt>
<a name="Direct.__call__"></a><div id="Direct.__call__-def"><a name="L163"></a><tt class="py-lineno">163</tt> <a class="py-toggle" href="#" id="Direct.__call__-toggle" onclick="return toggle('Direct.__call__');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.Direct-class.html#__call__">__call__</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Direct.__call__-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="Direct.__call__-expanded"><a name="L164"></a><tt class="py-lineno">164</tt>  <tt class="py-line">        <tt class="py-docstring">'''</tt> </tt>
<a name="L165"></a><tt class="py-lineno">165</tt>  <tt class="py-line"><tt class="py-docstring">        Transparently executes the search until the minimum is found. The stop</tt> </tt>
<a name="L166"></a><tt class="py-lineno">166</tt>  <tt class="py-line"><tt class="py-docstring">        criteria are the maximum error or the maximum number of iterations,</tt> </tt>
<a name="L167"></a><tt class="py-lineno">167</tt>  <tt class="py-line"><tt class="py-docstring">        whichever is reached first. Note that this is a ``__call__`` method, so</tt> </tt>
<a name="L168"></a><tt class="py-lineno">168</tt>  <tt class="py-line"><tt class="py-docstring">        the object is called as a function. This method returns a tuple</tt> </tt>
<a name="L169"></a><tt class="py-lineno">169</tt>  <tt class="py-line"><tt class="py-docstring">        ``(x, e)``, with the best estimate of the minimum and the error.</tt> </tt>
<a name="L170"></a><tt class="py-lineno">170</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L171"></a><tt class="py-lineno">171</tt>  <tt class="py-line"><tt class="py-docstring">        :Returns:</tt> </tt>
<a name="L172"></a><tt class="py-lineno">172</tt>  <tt class="py-line"><tt class="py-docstring">          This method returns a tuple ``(x, e)``, where ``x`` is the best</tt> </tt>
<a name="L173"></a><tt class="py-lineno">173</tt>  <tt class="py-line"><tt class="py-docstring">          estimate of the minimum, and ``e`` is the estimated error.</tt> </tt>
<a name="L174"></a><tt class="py-lineno">174</tt>  <tt class="py-line"><tt class="py-docstring">        '''</tt> </tt>
<a name="L175"></a><tt class="py-lineno">175</tt>  <tt class="py-line">        <tt class="py-name">emax</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__emax</tt> </tt>
<a name="L176"></a><tt class="py-lineno">176</tt>  <tt class="py-line">        <tt class="py-name">imax</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__imax</tt> </tt>
<a name="L177"></a><tt class="py-lineno">177</tt>  <tt class="py-line">        <tt class="py-name">i</tt> <tt class="py-op">=</tt> <tt class="py-number">0</tt> </tt>
<a name="L178"></a><tt class="py-lineno">178</tt>  <tt class="py-line">        <tt class="py-name">e</tt> <tt class="py-op">=</tt> <tt class="py-name">sum</tt><tt class="py-op">(</tt><tt id="link-30" class="py-name"><a title="peach.nn.rbfn.abs
peach.pso.base.abs" class="py-name" href="#" onclick="return doclink('link-30', 'abs', 'link-1');">abs</a></tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__dx</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
<a name="L179"></a><tt class="py-lineno">179</tt>  <tt class="py-line">        <tt class="py-keyword">while</tt> <tt class="py-name">e</tt> <tt class="py-op">&gt;</tt> <tt class="py-name">emax</tt><tt class="py-op">/</tt><tt class="py-number">2.</tt> <tt class="py-keyword">and</tt> <tt class="py-name">i</tt> <tt class="py-op">&lt;</tt> <tt class="py-name">imax</tt><tt class="py-op">:</tt> </tt>
<a name="L180"></a><tt class="py-lineno">180</tt>  <tt class="py-line">            <tt class="py-name">_</tt><tt class="py-op">,</tt> <tt class="py-name">e</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt id="link-31" class="py-name" targets="Method peach.fuzzy.cmeans.FuzzyCMeans.step()=peach.fuzzy.cmeans.FuzzyCMeans-class.html#step,Method peach.ga.base.GeneticAlgorithm.step()=peach.ga.base.GeneticAlgorithm-class.html#step,Method peach.nn.kmeans.KMeans.step()=peach.nn.kmeans.KMeans-class.html#step,Method peach.nn.mem.Hopfield.step()=peach.nn.mem.Hopfield-class.html#step,Method peach.optm.base.Optimizer.step()=peach.optm.base.Optimizer-class.html#step,Method peach.optm.linear.Direct1D.step()=peach.optm.linear.Direct1D-class.html#step,Method peach.optm.linear.Fibonacci.step()=peach.optm.linear.Fibonacci-class.html#step,Method peach.optm.linear.GoldenRule.step()=peach.optm.linear.GoldenRule-class.html#step,Method peach.optm.linear.Interpolation.step()=peach.optm.linear.Interpolation-class.html#step,Method peach.optm.multivar.Direct.step()=peach.optm.multivar.Direct-class.html#step,Method peach.optm.multivar.Gradient.step()=peach.optm.multivar.Gradient-class.html#step,Method peach.optm.multivar.MomentumGradient.step()=peach.optm.multivar.MomentumGradient-class.html#step,Method peach.optm.multivar.Newton.step()=peach.optm.multivar.Newton-class.html#step,Method peach.optm.quasinewton.BFGS.step()=peach.optm.quasinewton.BFGS-class.html#step,Method peach.optm.quasinewton.DFP.step()=peach.optm.quasinewton.DFP-class.html#step,Method peach.optm.quasinewton.SR1.step()=peach.optm.quasinewton.SR1-class.html#step,Method peach.optm.stochastic.CrossEntropy.step()=peach.optm.stochastic.CrossEntropy-class.html#step,Method peach.pso.base.ParticleSwarmOptimizer.step()=peach.pso.base.ParticleSwarmOptimizer-class.html#step,Method peach.sa.base.BinarySA.step()=peach.sa.base.BinarySA-class.html#step,Method peach.sa.base.ContinuousSA.step()=peach.sa.base.ContinuousSA-class.html#step"><a title="peach.fuzzy.cmeans.FuzzyCMeans.step
peach.ga.base.GeneticAlgorithm.step
peach.nn.kmeans.KMeans.step
peach.nn.mem.Hopfield.step
peach.optm.base.Optimizer.step
peach.optm.linear.Direct1D.step
peach.optm.linear.Fibonacci.step
peach.optm.linear.GoldenRule.step
peach.optm.linear.Interpolation.step
peach.optm.multivar.Direct.step
peach.optm.multivar.Gradient.step
peach.optm.multivar.MomentumGradient.step
peach.optm.multivar.Newton.step
peach.optm.quasinewton.BFGS.step
peach.optm.quasinewton.DFP.step
peach.optm.quasinewton.SR1.step
peach.optm.stochastic.CrossEntropy.step
peach.pso.base.ParticleSwarmOptimizer.step
peach.sa.base.BinarySA.step
peach.sa.base.ContinuousSA.step" class="py-name" href="#" onclick="return doclink('link-31', 'step', 'link-31');">step</a></tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L181"></a><tt class="py-lineno">181</tt>  <tt class="py-line">            <tt class="py-name">i</tt> <tt class="py-op">=</tt> <tt class="py-name">i</tt> <tt class="py-op">+</tt> <tt class="py-number">1</tt> </tt>
<a name="L182"></a><tt class="py-lineno">182</tt>  <tt class="py-line">        <tt class="py-keyword">return</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt><tt class="py-op">,</tt> <tt class="py-name">e</tt> </tt>
</div></div><a name="L183"></a><tt class="py-lineno">183</tt>  <tt class="py-line"> </tt>
<a name="L184"></a><tt class="py-lineno">184</tt>  <tt class="py-line"> </tt>
<a name="L185"></a><tt class="py-lineno">185</tt>  <tt class="py-line"><tt class="py-comment">################################################################################</tt> </tt>
<a name="Gradient"></a><div id="Gradient-def"><a name="L186"></a><tt class="py-lineno">186</tt> <a class="py-toggle" href="#" id="Gradient-toggle" onclick="return toggle('Gradient');">-</a><tt class="py-line"><tt class="py-keyword">class</tt> <a class="py-def-name" href="peach.optm.multivar.Gradient-class.html">Gradient</a><tt class="py-op">(</tt><tt class="py-base-class">Optimizer</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Gradient-collapsed" style="display:none;" pad="+++" indent="++++"></div><div id="Gradient-expanded"><a name="L187"></a><tt class="py-lineno">187</tt>  <tt class="py-line">    <tt class="py-docstring">'''</tt> </tt>
<a name="L188"></a><tt class="py-lineno">188</tt>  <tt class="py-line"><tt class="py-docstring">    Gradient search</tt> </tt>
<a name="L189"></a><tt class="py-lineno">189</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L190"></a><tt class="py-lineno">190</tt>  <tt class="py-line"><tt class="py-docstring">    This method uses the fact that the gradient of a function points to the</tt> </tt>
<a name="L191"></a><tt class="py-lineno">191</tt>  <tt class="py-line"><tt class="py-docstring">    direction of largest increase in the function (in general called *uphill*</tt> </tt>
<a name="L192"></a><tt class="py-lineno">192</tt>  <tt class="py-line"><tt class="py-docstring">    direction). So, the contrary direction (*downhill*) is used as search</tt> </tt>
<a name="L193"></a><tt class="py-lineno">193</tt>  <tt class="py-line"><tt class="py-docstring">    direction.</tt> </tt>
<a name="L194"></a><tt class="py-lineno">194</tt>  <tt class="py-line"><tt class="py-docstring">    '''</tt> </tt>
<a name="Gradient.__init__"></a><div id="Gradient.__init__-def"><a name="L195"></a><tt class="py-lineno">195</tt> <a class="py-toggle" href="#" id="Gradient.__init__-toggle" onclick="return toggle('Gradient.__init__');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.Gradient-class.html#__init__">__init__</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">f</tt><tt class="py-op">,</tt> <tt class="py-param">x0</tt><tt class="py-op">,</tt> <tt class="py-param">ranges</tt><tt class="py-op">=</tt><tt class="py-name">None</tt><tt class="py-op">,</tt> <tt class="py-param">df</tt><tt class="py-op">=</tt><tt class="py-name">None</tt><tt class="py-op">,</tt> <tt class="py-param">h</tt><tt class="py-op">=</tt><tt class="py-number">0.1</tt><tt class="py-op">,</tt> <tt class="py-param">emax</tt><tt class="py-op">=</tt><tt class="py-number">1e-5</tt><tt class="py-op">,</tt> <tt class="py-param">imax</tt><tt class="py-op">=</tt><tt class="py-number">1000</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Gradient.__init__-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="Gradient.__init__-expanded"><a name="L196"></a><tt class="py-lineno">196</tt>  <tt class="py-line">        <tt class="py-docstring">'''</tt> </tt>
<a name="L197"></a><tt class="py-lineno">197</tt>  <tt class="py-line"><tt class="py-docstring">        Initializes the optimizer.</tt> </tt>
<a name="L198"></a><tt class="py-lineno">198</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L199"></a><tt class="py-lineno">199</tt>  <tt class="py-line"><tt class="py-docstring">        To create an optimizer of this type, instantiate the class with the</tt> </tt>
<a name="L200"></a><tt class="py-lineno">200</tt>  <tt class="py-line"><tt class="py-docstring">        parameters given below:</tt> </tt>
<a name="L201"></a><tt class="py-lineno">201</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L202"></a><tt class="py-lineno">202</tt>  <tt class="py-line"><tt class="py-docstring">        :Parameters:</tt> </tt>
<a name="L203"></a><tt class="py-lineno">203</tt>  <tt class="py-line"><tt class="py-docstring">          f</tt> </tt>
<a name="L204"></a><tt class="py-lineno">204</tt>  <tt class="py-line"><tt class="py-docstring">            A multivariable function to be optimized. The function should have</tt> </tt>
<a name="L205"></a><tt class="py-lineno">205</tt>  <tt class="py-line"><tt class="py-docstring">            only one parameter, a multidimensional line-vector, and return the</tt> </tt>
<a name="L206"></a><tt class="py-lineno">206</tt>  <tt class="py-line"><tt class="py-docstring">            function value, a scalar.</tt> </tt>
<a name="L207"></a><tt class="py-lineno">207</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L208"></a><tt class="py-lineno">208</tt>  <tt class="py-line"><tt class="py-docstring">          x0</tt> </tt>
<a name="L209"></a><tt class="py-lineno">209</tt>  <tt class="py-line"><tt class="py-docstring">            First estimate of the minimum. Estimates can be given in any format,</tt> </tt>
<a name="L210"></a><tt class="py-lineno">210</tt>  <tt class="py-line"><tt class="py-docstring">            but internally they are converted to a one-dimension vector, where</tt> </tt>
<a name="L211"></a><tt class="py-lineno">211</tt>  <tt class="py-line"><tt class="py-docstring">            each component corresponds to the estimate of that particular</tt> </tt>
<a name="L212"></a><tt class="py-lineno">212</tt>  <tt class="py-line"><tt class="py-docstring">            variable. The vector is computed by flattening the array.</tt> </tt>
<a name="L213"></a><tt class="py-lineno">213</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L214"></a><tt class="py-lineno">214</tt>  <tt class="py-line"><tt class="py-docstring">          ranges</tt> </tt>
<a name="L215"></a><tt class="py-lineno">215</tt>  <tt class="py-line"><tt class="py-docstring">            A range of values might be passed to the algorithm, but it is not</tt> </tt>
<a name="L216"></a><tt class="py-lineno">216</tt>  <tt class="py-line"><tt class="py-docstring">            necessary. If supplied, this parameter should be a list of ranges</tt> </tt>
<a name="L217"></a><tt class="py-lineno">217</tt>  <tt class="py-line"><tt class="py-docstring">            for each variable of the objective function. It is specified as a</tt> </tt>
<a name="L218"></a><tt class="py-lineno">218</tt>  <tt class="py-line"><tt class="py-docstring">            list of tuples of two values, ``(x0, x1)``, where ``x0`` is the</tt> </tt>
<a name="L219"></a><tt class="py-lineno">219</tt>  <tt class="py-line"><tt class="py-docstring">            start of the interval, and ``x1`` its end. Obviously, ``x0`` should</tt> </tt>
<a name="L220"></a><tt class="py-lineno">220</tt>  <tt class="py-line"><tt class="py-docstring">            be smaller than ``x1``. It can also be given as a list with a simple</tt> </tt>
<a name="L221"></a><tt class="py-lineno">221</tt>  <tt class="py-line"><tt class="py-docstring">            tuple in the same format. In that case, the same range will be</tt> </tt>
<a name="L222"></a><tt class="py-lineno">222</tt>  <tt class="py-line"><tt class="py-docstring">            applied for every variable in the optimization.</tt> </tt>
<a name="L223"></a><tt class="py-lineno">223</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L224"></a><tt class="py-lineno">224</tt>  <tt class="py-line"><tt class="py-docstring">          df</tt> </tt>
<a name="L225"></a><tt class="py-lineno">225</tt>  <tt class="py-line"><tt class="py-docstring">            A function to calculate the gradient vector of the cost function</tt> </tt>
<a name="L226"></a><tt class="py-lineno">226</tt>  <tt class="py-line"><tt class="py-docstring">            ``f``. Defaults to ``None``, if no gradient is supplied, then it is</tt> </tt>
<a name="L227"></a><tt class="py-lineno">227</tt>  <tt class="py-line"><tt class="py-docstring">            estimated from the cost function using Euler equations.</tt> </tt>
<a name="L228"></a><tt class="py-lineno">228</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L229"></a><tt class="py-lineno">229</tt>  <tt class="py-line"><tt class="py-docstring">          h</tt> </tt>
<a name="L230"></a><tt class="py-lineno">230</tt>  <tt class="py-line"><tt class="py-docstring">            Convergence step. This method does not takes into consideration the</tt> </tt>
<a name="L231"></a><tt class="py-lineno">231</tt>  <tt class="py-line"><tt class="py-docstring">            possibility of varying the convergence step, to avoid Stiefel cages.</tt> </tt>
<a name="L232"></a><tt class="py-lineno">232</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L233"></a><tt class="py-lineno">233</tt>  <tt class="py-line"><tt class="py-docstring">          emax</tt> </tt>
<a name="L234"></a><tt class="py-lineno">234</tt>  <tt class="py-line"><tt class="py-docstring">            Maximum allowed error. The algorithm stops as soon as the error is</tt> </tt>
<a name="L235"></a><tt class="py-lineno">235</tt>  <tt class="py-line"><tt class="py-docstring">            below this level. The error is absolute.</tt> </tt>
<a name="L236"></a><tt class="py-lineno">236</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L237"></a><tt class="py-lineno">237</tt>  <tt class="py-line"><tt class="py-docstring">          imax</tt> </tt>
<a name="L238"></a><tt class="py-lineno">238</tt>  <tt class="py-line"><tt class="py-docstring">            Maximum number of iterations, the algorithm stops as soon this</tt> </tt>
<a name="L239"></a><tt class="py-lineno">239</tt>  <tt class="py-line"><tt class="py-docstring">            number of iterations are executed, no matter what the error is at</tt> </tt>
<a name="L240"></a><tt class="py-lineno">240</tt>  <tt class="py-line"><tt class="py-docstring">            the moment.</tt> </tt>
<a name="L241"></a><tt class="py-lineno">241</tt>  <tt class="py-line"><tt class="py-docstring">        '''</tt> </tt>
<a name="L242"></a><tt class="py-lineno">242</tt>  <tt class="py-line">        <tt id="link-32" class="py-name"><a title="peach.optm.base.Optimizer" class="py-name" href="#" onclick="return doclink('link-32', 'Optimizer', 'link-3');">Optimizer</a></tt><tt class="py-op">.</tt><tt id="link-33" class="py-name"><a title="peach.fuzzy.base.FuzzySet.__init__
peach.fuzzy.cmeans.FuzzyCMeans.__init__
peach.fuzzy.control.Controller.__init__
peach.fuzzy.control.Parametric.__init__
peach.fuzzy.mf.Bell.__init__
peach.fuzzy.mf.DecreasingRamp.__init__
peach.fuzzy.mf.DecreasingSigmoid.__init__
peach.fuzzy.mf.Gaussian.__init__
peach.fuzzy.mf.IncreasingRamp.__init__
peach.fuzzy.mf.IncreasingSigmoid.__init__
peach.fuzzy.mf.Membership.__init__
peach.fuzzy.mf.RaisedCosine.__init__
peach.fuzzy.mf.Smf.__init__
peach.fuzzy.mf.Trapezoid.__init__
peach.fuzzy.mf.Triangle.__init__
peach.fuzzy.mf.Zmf.__init__
peach.ga.base.GeneticAlgorithm.__init__
peach.ga.chromosome.Chromosome.__init__
peach.ga.crossover.OnePoint.__init__
peach.ga.crossover.TwoPoint.__init__
peach.ga.crossover.Uniform.__init__
peach.ga.fitness.Fitness.__init__
peach.ga.fitness.Ranking.__init__
peach.ga.mutation.BitToBit.__init__
peach.nn.af.Activation.__init__
peach.nn.af.ArcTan.__init__
peach.nn.af.Gaussian.__init__
peach.nn.af.Linear.__init__
peach.nn.af.Ramp.__init__
peach.nn.af.Sigmoid.__init__
peach.nn.af.Signum.__init__
peach.nn.af.TanH.__init__
peach.nn.af.Threshold.__init__
peach.nn.base.Layer.__init__
peach.nn.kmeans.KMeans.__init__
peach.nn.lrules.BackPropagation.__init__
peach.nn.lrules.Competitive.__init__
peach.nn.lrules.Cooperative.__init__
peach.nn.lrules.LMS.__init__
peach.nn.lrules.WinnerTakesAll.__init__
peach.nn.mem.Hopfield.__init__
peach.nn.nnet.FeedForward.__init__
peach.nn.nnet.GRNN.__init__
peach.nn.nnet.PNN.__init__
peach.nn.nnet.SOM.__init__
peach.nn.rbfn.RBFN.__init__
peach.optm.base.Optimizer.__init__
peach.optm.linear.Direct1D.__init__
peach.optm.linear.Fibonacci.__init__
peach.optm.linear.GoldenRule.__init__
peach.optm.linear.Interpolation.__init__
peach.optm.multivar.Direct.__init__
peach.optm.multivar.Gradient.__init__
peach.optm.multivar.MomentumGradient.__init__
peach.optm.multivar.Newton.__init__
peach.optm.quasinewton.BFGS.__init__
peach.optm.quasinewton.DFP.__init__
peach.optm.quasinewton.SR1.__init__
peach.optm.stochastic.CrossEntropy.__init__
peach.pso.acc.Accelerator.__init__
peach.pso.acc.StandardPSO.__init__
peach.pso.base.ParticleSwarmOptimizer.__init__
peach.sa.base.BinarySA.__init__
peach.sa.base.ContinuousSA.__init__
peach.sa.neighbor.BinaryNeighbor.__init__
peach.sa.neighbor.ContinuousNeighbor.__init__
peach.sa.neighbor.GaussianNeighbor.__init__
peach.sa.neighbor.InvertBitsNeighbor.__init__
peach.sa.neighbor.UniformNeighbor.__init__" class="py-name" href="#" onclick="return doclink('link-33', '__init__', 'link-7');">__init__</a></tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">)</tt> </tt>
<a name="L243"></a><tt class="py-lineno">243</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__f</tt> <tt class="py-op">=</tt> <tt class="py-name">f</tt> </tt>
<a name="L244"></a><tt class="py-lineno">244</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt> <tt class="py-op">=</tt> <tt class="py-name">array</tt><tt class="py-op">(</tt><tt class="py-name">x0</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt class="py-name">ravel</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L245"></a><tt class="py-lineno">245</tt>  <tt class="py-line">        <tt class="py-keyword">if</tt> <tt class="py-name">df</tt> <tt class="py-keyword">is</tt> <tt class="py-name">None</tt><tt class="py-op">:</tt> </tt>
<a name="L246"></a><tt class="py-lineno">246</tt>  <tt class="py-line">            <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__df</tt> <tt class="py-op">=</tt> <tt id="link-34" class="py-name"><a title="peach.optm.base.gradient" class="py-name" href="#" onclick="return doclink('link-34', 'gradient', 'link-4');">gradient</a></tt><tt class="py-op">(</tt><tt class="py-name">f</tt><tt class="py-op">)</tt> </tt>
<a name="L247"></a><tt class="py-lineno">247</tt>  <tt class="py-line">        <tt class="py-keyword">else</tt><tt class="py-op">:</tt> </tt>
<a name="L248"></a><tt class="py-lineno">248</tt>  <tt class="py-line">            <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__df</tt> <tt class="py-op">=</tt> <tt class="py-name">df</tt> </tt>
<a name="L249"></a><tt class="py-lineno">249</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__h</tt> <tt class="py-op">=</tt> <tt class="py-name">h</tt> </tt>
<a name="L250"></a><tt class="py-lineno">250</tt>  <tt class="py-line"> </tt>
<a name="L251"></a><tt class="py-lineno">251</tt>  <tt class="py-line">        <tt class="py-comment"># Determine ranges of the variables</tt> </tt>
<a name="L252"></a><tt class="py-lineno">252</tt>  <tt class="py-line">        <tt class="py-keyword">if</tt> <tt class="py-name">ranges</tt> <tt class="py-keyword">is</tt> <tt class="py-keyword">not</tt> <tt class="py-name">None</tt><tt class="py-op">:</tt> </tt>
<a name="L253"></a><tt class="py-lineno">253</tt>  <tt class="py-line">            <tt class="py-name">ranges</tt> <tt class="py-op">=</tt> <tt class="py-name">list</tt><tt class="py-op">(</tt><tt class="py-name">ranges</tt><tt class="py-op">)</tt> </tt>
<a name="L254"></a><tt class="py-lineno">254</tt>  <tt class="py-line">            <tt class="py-keyword">if</tt> <tt class="py-name">len</tt><tt class="py-op">(</tt><tt class="py-name">ranges</tt><tt class="py-op">)</tt> <tt class="py-op">==</tt> <tt class="py-number">1</tt><tt class="py-op">:</tt> </tt>
<a name="L255"></a><tt class="py-lineno">255</tt>  <tt class="py-line">                <tt class="py-name">ranges</tt> <tt class="py-op">=</tt> <tt class="py-name">array</tt><tt class="py-op">(</tt><tt class="py-name">ranges</tt> <tt class="py-op">*</tt> <tt class="py-name">len</tt><tt class="py-op">(</tt><tt class="py-name">x0</tt><tt class="py-op">[</tt><tt class="py-number">0</tt><tt class="py-op">]</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
<a name="L256"></a><tt class="py-lineno">256</tt>  <tt class="py-line">            <tt class="py-keyword">else</tt><tt class="py-op">:</tt> </tt>
<a name="L257"></a><tt class="py-lineno">257</tt>  <tt class="py-line">                <tt class="py-name">ranges</tt> <tt class="py-op">=</tt> <tt class="py-name">array</tt><tt class="py-op">(</tt><tt class="py-name">ranges</tt><tt class="py-op">)</tt> </tt>
<a name="L258"></a><tt class="py-lineno">258</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">ranges</tt> <tt class="py-op">=</tt> <tt class="py-name">ranges</tt> </tt>
<a name="L259"></a><tt class="py-lineno">259</tt>  <tt class="py-line">        <tt class="py-string">'''Holds the ranges for every variable. Although it is a writable</tt> </tt>
<a name="L260"></a><tt class="py-lineno">260</tt>  <tt class="py-line"><tt class="py-string">        property, care should be taken in changing parameters before ending the</tt> </tt>
<a name="L261"></a><tt class="py-lineno">261</tt>  <tt class="py-line"><tt class="py-string">        convergence.'''</tt> </tt>
<a name="L262"></a><tt class="py-lineno">262</tt>  <tt class="py-line"> </tt>
<a name="L263"></a><tt class="py-lineno">263</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__emax</tt> <tt class="py-op">=</tt> <tt class="py-name">float</tt><tt class="py-op">(</tt><tt class="py-name">emax</tt><tt class="py-op">)</tt> </tt>
<a name="L264"></a><tt class="py-lineno">264</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__imax</tt> <tt class="py-op">=</tt> <tt class="py-name">int</tt><tt class="py-op">(</tt><tt class="py-name">imax</tt><tt class="py-op">)</tt> </tt>
</div><a name="L265"></a><tt class="py-lineno">265</tt>  <tt class="py-line"> </tt>
<a name="L266"></a><tt class="py-lineno">266</tt>  <tt class="py-line"> </tt>
<a name="Gradient.__get_x"></a><div id="Gradient.__get_x-def"><a name="L267"></a><tt class="py-lineno">267</tt> <a class="py-toggle" href="#" id="Gradient.__get_x-toggle" onclick="return toggle('Gradient.__get_x');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.Gradient-class.html#__get_x">__get_x</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Gradient.__get_x-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="Gradient.__get_x-expanded"><a name="L268"></a><tt class="py-lineno">268</tt>  <tt class="py-line">        <tt class="py-keyword">return</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt> </tt>
</div><a name="L269"></a><tt class="py-lineno">269</tt>  <tt class="py-line"> </tt>
<a name="Gradient.__set_x"></a><div id="Gradient.__set_x-def"><a name="L270"></a><tt class="py-lineno">270</tt> <a class="py-toggle" href="#" id="Gradient.__set_x-toggle" onclick="return toggle('Gradient.__set_x');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.Gradient-class.html#__set_x">__set_x</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">x0</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Gradient.__set_x-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="Gradient.__set_x-expanded"><a name="L271"></a><tt class="py-lineno">271</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt id="link-35" class="py-name"><a title="peach.ga.base.GeneticAlgorithm.restart
peach.optm.linear.Direct1D.restart
peach.optm.linear.Fibonacci.restart
peach.optm.linear.GoldenRule.restart
peach.optm.linear.Interpolation.restart
peach.optm.multivar.Direct.restart
peach.optm.multivar.Gradient.restart
peach.optm.multivar.MomentumGradient.restart
peach.optm.multivar.Newton.restart
peach.optm.quasinewton.BFGS.restart
peach.optm.quasinewton.DFP.restart
peach.optm.quasinewton.SR1.restart
peach.pso.base.ParticleSwarmOptimizer.restart
peach.sa.base.BinarySA.restart
peach.sa.base.ContinuousSA.restart" class="py-name" href="#" onclick="return doclink('link-35', 'restart', 'link-10');">restart</a></tt><tt class="py-op">(</tt><tt class="py-name">x0</tt><tt class="py-op">)</tt> </tt>
</div><a name="L272"></a><tt class="py-lineno">272</tt>  <tt class="py-line"> </tt>
<a name="L273"></a><tt class="py-lineno">273</tt>  <tt class="py-line">    <tt id="link-36" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-36', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt class="py-name">property</tt><tt class="py-op">(</tt><tt id="link-37" class="py-name"><a title="peach.optm.linear.Direct1D.__get_x
peach.optm.linear.GoldenRule.__get_x
peach.optm.linear.Interpolation.__get_x
peach.optm.multivar.Direct.__get_x
peach.optm.multivar.Gradient.__get_x
peach.optm.multivar.MomentumGradient.__get_x
peach.optm.multivar.Newton.__get_x
peach.optm.quasinewton.DFP.__get_x
peach.optm.quasinewton.SR1.__get_x
peach.sa.base.BinarySA.__get_x
peach.sa.base.ContinuousSA.__get_x" class="py-name" href="#" onclick="return doclink('link-37', '__get_x', 'link-12');">__get_x</a></tt><tt class="py-op">,</tt> <tt id="link-38" class="py-name"><a title="peach.optm.linear.Direct1D.__set_x
peach.optm.linear.GoldenRule.__set_x
peach.optm.linear.Interpolation.__set_x
peach.optm.multivar.Direct.__set_x
peach.optm.multivar.Gradient.__set_x
peach.optm.multivar.MomentumGradient.__set_x
peach.optm.multivar.Newton.__set_x
peach.optm.quasinewton.DFP.__set_x
peach.optm.quasinewton.SR1.__set_x
peach.sa.base.BinarySA.__set_x
peach.sa.base.ContinuousSA.__set_x" class="py-name" href="#" onclick="return doclink('link-38', '__set_x', 'link-13');">__set_x</a></tt><tt class="py-op">)</tt> </tt>
<a name="L274"></a><tt class="py-lineno">274</tt>  <tt class="py-line">    <tt class="py-string">'''The estimate of the position of the minimum.'''</tt> </tt>
<a name="L275"></a><tt class="py-lineno">275</tt>  <tt class="py-line"> </tt>
<a name="L276"></a><tt class="py-lineno">276</tt>  <tt class="py-line"> </tt>
<a name="Gradient.restart"></a><div id="Gradient.restart-def"><a name="L277"></a><tt class="py-lineno">277</tt> <a class="py-toggle" href="#" id="Gradient.restart-toggle" onclick="return toggle('Gradient.restart');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.Gradient-class.html#restart">restart</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">x0</tt><tt class="py-op">,</tt> <tt class="py-param">h</tt><tt class="py-op">=</tt><tt class="py-name">None</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Gradient.restart-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="Gradient.restart-expanded"><a name="L278"></a><tt class="py-lineno">278</tt>  <tt class="py-line">        <tt class="py-docstring">'''</tt> </tt>
<a name="L279"></a><tt class="py-lineno">279</tt>  <tt class="py-line"><tt class="py-docstring">        Resets the optimizer, returning to its original state, and allowing to</tt> </tt>
<a name="L280"></a><tt class="py-lineno">280</tt>  <tt class="py-line"><tt class="py-docstring">        use a new first estimate.</tt> </tt>
<a name="L281"></a><tt class="py-lineno">281</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L282"></a><tt class="py-lineno">282</tt>  <tt class="py-line"><tt class="py-docstring">        :Parameters:</tt> </tt>
<a name="L283"></a><tt class="py-lineno">283</tt>  <tt class="py-line"><tt class="py-docstring">          x0</tt> </tt>
<a name="L284"></a><tt class="py-lineno">284</tt>  <tt class="py-line"><tt class="py-docstring">            New estimate of the minimum. Estimates can be given in any format,</tt> </tt>
<a name="L285"></a><tt class="py-lineno">285</tt>  <tt class="py-line"><tt class="py-docstring">            but internally they are converted to a one-dimension vector, where</tt> </tt>
<a name="L286"></a><tt class="py-lineno">286</tt>  <tt class="py-line"><tt class="py-docstring">            each component corresponds to the estimate of that particular</tt> </tt>
<a name="L287"></a><tt class="py-lineno">287</tt>  <tt class="py-line"><tt class="py-docstring">            variable. The vector is computed by flattening the array.</tt> </tt>
<a name="L288"></a><tt class="py-lineno">288</tt>  <tt class="py-line"><tt class="py-docstring">          h</tt> </tt>
<a name="L289"></a><tt class="py-lineno">289</tt>  <tt class="py-line"><tt class="py-docstring">            Convergence step. This method does not takes into consideration the</tt> </tt>
<a name="L290"></a><tt class="py-lineno">290</tt>  <tt class="py-line"><tt class="py-docstring">            possibility of varying the convergence step, to avoid Stiefel cages.</tt> </tt>
<a name="L291"></a><tt class="py-lineno">291</tt>  <tt class="py-line"><tt class="py-docstring">        '''</tt> </tt>
<a name="L292"></a><tt class="py-lineno">292</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt> <tt class="py-op">=</tt> <tt class="py-name">array</tt><tt class="py-op">(</tt><tt class="py-name">x0</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt class="py-name">ravel</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L293"></a><tt class="py-lineno">293</tt>  <tt class="py-line">        <tt class="py-keyword">if</tt> <tt class="py-name">h</tt> <tt class="py-keyword">is</tt> <tt class="py-keyword">not</tt> <tt class="py-name">None</tt><tt class="py-op">:</tt> </tt>
<a name="L294"></a><tt class="py-lineno">294</tt>  <tt class="py-line">            <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__h</tt> <tt class="py-op">=</tt> <tt class="py-name">h</tt> </tt>
</div><a name="L295"></a><tt class="py-lineno">295</tt>  <tt class="py-line"> </tt>
<a name="L296"></a><tt class="py-lineno">296</tt>  <tt class="py-line"> </tt>
<a name="Gradient.step"></a><div id="Gradient.step-def"><a name="L297"></a><tt class="py-lineno">297</tt> <a class="py-toggle" href="#" id="Gradient.step-toggle" onclick="return toggle('Gradient.step');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.Gradient-class.html#step">step</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Gradient.step-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="Gradient.step-expanded"><a name="L298"></a><tt class="py-lineno">298</tt>  <tt class="py-line">        <tt class="py-docstring">'''</tt> </tt>
<a name="L299"></a><tt class="py-lineno">299</tt>  <tt class="py-line"><tt class="py-docstring">        One step of the search.</tt> </tt>
<a name="L300"></a><tt class="py-lineno">300</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L301"></a><tt class="py-lineno">301</tt>  <tt class="py-line"><tt class="py-docstring">        In this method, the result of the step is dependent only of the given</tt> </tt>
<a name="L302"></a><tt class="py-lineno">302</tt>  <tt class="py-line"><tt class="py-docstring">        estimated, so it can be used for different kind of investigations on the</tt> </tt>
<a name="L303"></a><tt class="py-lineno">303</tt>  <tt class="py-line"><tt class="py-docstring">        same cost function.</tt> </tt>
<a name="L304"></a><tt class="py-lineno">304</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L305"></a><tt class="py-lineno">305</tt>  <tt class="py-line"><tt class="py-docstring">        :Returns:</tt> </tt>
<a name="L306"></a><tt class="py-lineno">306</tt>  <tt class="py-line"><tt class="py-docstring">          This method returns a tuple ``(x, e)``, where ``x`` is the updated</tt> </tt>
<a name="L307"></a><tt class="py-lineno">307</tt>  <tt class="py-line"><tt class="py-docstring">          estimate of the minimum, and ``e`` is the estimated error.</tt> </tt>
<a name="L308"></a><tt class="py-lineno">308</tt>  <tt class="py-line"><tt class="py-docstring">        '''</tt> </tt>
<a name="L309"></a><tt class="py-lineno">309</tt>  <tt class="py-line">        <tt id="link-39" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-39', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt> </tt>
<a name="L310"></a><tt class="py-lineno">310</tt>  <tt class="py-line">        <tt class="py-name">xold</tt> <tt class="py-op">=</tt> <tt id="link-40" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-40', 'x', 'link-11');">x</a></tt> </tt>
<a name="L311"></a><tt class="py-lineno">311</tt>  <tt class="py-line"> </tt>
<a name="L312"></a><tt class="py-lineno">312</tt>  <tt class="py-line">        <tt class="py-comment"># New estimate</tt> </tt>
<a name="L313"></a><tt class="py-lineno">313</tt>  <tt class="py-line">        <tt id="link-41" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-41', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt id="link-42" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-42', 'x', 'link-11');">x</a></tt> <tt class="py-op">-</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__h</tt> <tt class="py-op">*</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__df</tt><tt class="py-op">(</tt><tt id="link-43" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-43', 'x', 'link-11');">x</a></tt><tt class="py-op">)</tt> </tt>
<a name="L314"></a><tt class="py-lineno">314</tt>  <tt class="py-line"> </tt>
<a name="L315"></a><tt class="py-lineno">315</tt>  <tt class="py-line">        <tt class="py-comment"># Sanity check</tt> </tt>
<a name="L316"></a><tt class="py-lineno">316</tt>  <tt class="py-line">        <tt class="py-keyword">if</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">ranges</tt> <tt class="py-keyword">is</tt> <tt class="py-keyword">not</tt> <tt class="py-name">None</tt><tt class="py-op">:</tt> </tt>
<a name="L317"></a><tt class="py-lineno">317</tt>  <tt class="py-line">            <tt class="py-name">r0</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">ranges</tt><tt class="py-op">[</tt><tt class="py-op">:</tt><tt class="py-op">,</tt> <tt class="py-number">0</tt><tt class="py-op">]</tt> </tt>
<a name="L318"></a><tt class="py-lineno">318</tt>  <tt class="py-line">            <tt class="py-name">r1</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">ranges</tt><tt class="py-op">[</tt><tt class="py-op">:</tt><tt class="py-op">,</tt> <tt class="py-number">1</tt><tt class="py-op">]</tt> </tt>
<a name="L319"></a><tt class="py-lineno">319</tt>  <tt class="py-line">            <tt id="link-44" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-44', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt class="py-name">where</tt><tt class="py-op">(</tt><tt id="link-45" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-45', 'x', 'link-11');">x</a></tt> <tt class="py-op">&lt;</tt> <tt class="py-name">r0</tt><tt class="py-op">,</tt> <tt class="py-name">r0</tt><tt class="py-op">,</tt> <tt id="link-46" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-46', 'x', 'link-11');">x</a></tt><tt class="py-op">)</tt> </tt>
<a name="L320"></a><tt class="py-lineno">320</tt>  <tt class="py-line">            <tt id="link-47" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-47', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt class="py-name">where</tt><tt class="py-op">(</tt><tt id="link-48" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-48', 'x', 'link-11');">x</a></tt> <tt class="py-op">&gt;</tt> <tt class="py-name">r1</tt><tt class="py-op">,</tt> <tt class="py-name">r1</tt><tt class="py-op">,</tt> <tt id="link-49" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-49', 'x', 'link-11');">x</a></tt><tt class="py-op">)</tt> </tt>
<a name="L321"></a><tt class="py-lineno">321</tt>  <tt class="py-line"> </tt>
<a name="L322"></a><tt class="py-lineno">322</tt>  <tt class="py-line">        <tt class="py-comment"># Update state</tt> </tt>
<a name="L323"></a><tt class="py-lineno">323</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt> <tt class="py-op">=</tt> <tt id="link-50" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-50', 'x', 'link-11');">x</a></tt> </tt>
<a name="L324"></a><tt class="py-lineno">324</tt>  <tt class="py-line">        <tt class="py-keyword">return</tt> <tt id="link-51" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-51', 'x', 'link-11');">x</a></tt><tt class="py-op">,</tt> <tt class="py-name">sum</tt><tt class="py-op">(</tt><tt id="link-52" class="py-name"><a title="peach.nn.rbfn.abs
peach.pso.base.abs" class="py-name" href="#" onclick="return doclink('link-52', 'abs', 'link-1');">abs</a></tt><tt class="py-op">(</tt><tt id="link-53" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-53', 'x', 'link-11');">x</a></tt> <tt class="py-op">-</tt> <tt class="py-name">xold</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
</div><a name="L325"></a><tt class="py-lineno">325</tt>  <tt class="py-line"> </tt>
<a name="L326"></a><tt class="py-lineno">326</tt>  <tt class="py-line"> </tt>
<a name="Gradient.__call__"></a><div id="Gradient.__call__-def"><a name="L327"></a><tt class="py-lineno">327</tt> <a class="py-toggle" href="#" id="Gradient.__call__-toggle" onclick="return toggle('Gradient.__call__');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.Gradient-class.html#__call__">__call__</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Gradient.__call__-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="Gradient.__call__-expanded"><a name="L328"></a><tt class="py-lineno">328</tt>  <tt class="py-line">        <tt class="py-docstring">'''</tt> </tt>
<a name="L329"></a><tt class="py-lineno">329</tt>  <tt class="py-line"><tt class="py-docstring">        Transparently executes the search until the minimum is found. The stop</tt> </tt>
<a name="L330"></a><tt class="py-lineno">330</tt>  <tt class="py-line"><tt class="py-docstring">        criteria are the maximum error or the maximum number of iterations,</tt> </tt>
<a name="L331"></a><tt class="py-lineno">331</tt>  <tt class="py-line"><tt class="py-docstring">        whichever is reached first. Note that this is a ``__call__`` method, so</tt> </tt>
<a name="L332"></a><tt class="py-lineno">332</tt>  <tt class="py-line"><tt class="py-docstring">        the object is called as a function. This method returns a tuple</tt> </tt>
<a name="L333"></a><tt class="py-lineno">333</tt>  <tt class="py-line"><tt class="py-docstring">        ``(x, e)``, with the best estimate of the minimum and the error.</tt> </tt>
<a name="L334"></a><tt class="py-lineno">334</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L335"></a><tt class="py-lineno">335</tt>  <tt class="py-line"><tt class="py-docstring">        :Returns:</tt> </tt>
<a name="L336"></a><tt class="py-lineno">336</tt>  <tt class="py-line"><tt class="py-docstring">          This method returns a tuple ``(x, e)``, where ``x`` is the best</tt> </tt>
<a name="L337"></a><tt class="py-lineno">337</tt>  <tt class="py-line"><tt class="py-docstring">          estimate of the minimum, and ``e`` is the estimated error.</tt> </tt>
<a name="L338"></a><tt class="py-lineno">338</tt>  <tt class="py-line"><tt class="py-docstring">        '''</tt> </tt>
<a name="L339"></a><tt class="py-lineno">339</tt>  <tt class="py-line">        <tt class="py-name">emax</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__emax</tt> </tt>
<a name="L340"></a><tt class="py-lineno">340</tt>  <tt class="py-line">        <tt class="py-name">imax</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__imax</tt> </tt>
<a name="L341"></a><tt class="py-lineno">341</tt>  <tt class="py-line">        <tt class="py-name">e</tt> <tt class="py-op">=</tt> <tt class="py-name">emax</tt> </tt>
<a name="L342"></a><tt class="py-lineno">342</tt>  <tt class="py-line">        <tt class="py-name">i</tt> <tt class="py-op">=</tt> <tt class="py-number">0</tt> </tt>
<a name="L343"></a><tt class="py-lineno">343</tt>  <tt class="py-line">        <tt class="py-keyword">while</tt> <tt class="py-name">e</tt> <tt class="py-op">&gt;</tt> <tt class="py-name">emax</tt><tt class="py-op">/</tt><tt class="py-number">2.</tt> <tt class="py-keyword">and</tt> <tt class="py-name">i</tt> <tt class="py-op">&lt;</tt> <tt class="py-name">imax</tt><tt class="py-op">:</tt> </tt>
<a name="L344"></a><tt class="py-lineno">344</tt>  <tt class="py-line">            <tt class="py-name">_</tt><tt class="py-op">,</tt> <tt class="py-name">e</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt id="link-54" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.step
peach.ga.base.GeneticAlgorithm.step
peach.nn.kmeans.KMeans.step
peach.nn.mem.Hopfield.step
peach.optm.base.Optimizer.step
peach.optm.linear.Direct1D.step
peach.optm.linear.Fibonacci.step
peach.optm.linear.GoldenRule.step
peach.optm.linear.Interpolation.step
peach.optm.multivar.Direct.step
peach.optm.multivar.Gradient.step
peach.optm.multivar.MomentumGradient.step
peach.optm.multivar.Newton.step
peach.optm.quasinewton.BFGS.step
peach.optm.quasinewton.DFP.step
peach.optm.quasinewton.SR1.step
peach.optm.stochastic.CrossEntropy.step
peach.pso.base.ParticleSwarmOptimizer.step
peach.sa.base.BinarySA.step
peach.sa.base.ContinuousSA.step" class="py-name" href="#" onclick="return doclink('link-54', 'step', 'link-31');">step</a></tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L345"></a><tt class="py-lineno">345</tt>  <tt class="py-line">            <tt class="py-name">i</tt> <tt class="py-op">=</tt> <tt class="py-name">i</tt> <tt class="py-op">+</tt> <tt class="py-number">1</tt> </tt>
<a name="L346"></a><tt class="py-lineno">346</tt>  <tt class="py-line">        <tt class="py-keyword">return</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt><tt class="py-op">,</tt> <tt class="py-name">e</tt> </tt>
</div></div><a name="L347"></a><tt class="py-lineno">347</tt>  <tt class="py-line"> </tt>
<a name="L348"></a><tt class="py-lineno">348</tt>  <tt class="py-line"> </tt>
<a name="L349"></a><tt class="py-lineno">349</tt>  <tt class="py-line"><tt class="py-comment">################################################################################</tt> </tt>
<a name="MomentumGradient"></a><div id="MomentumGradient-def"><a name="L350"></a><tt class="py-lineno">350</tt> <a class="py-toggle" href="#" id="MomentumGradient-toggle" onclick="return toggle('MomentumGradient');">-</a><tt class="py-line"><tt class="py-keyword">class</tt> <a class="py-def-name" href="peach.optm.multivar.MomentumGradient-class.html">MomentumGradient</a><tt class="py-op">(</tt><tt class="py-base-class">Optimizer</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="MomentumGradient-collapsed" style="display:none;" pad="+++" indent="++++"></div><div id="MomentumGradient-expanded"><a name="L351"></a><tt class="py-lineno">351</tt>  <tt class="py-line">    <tt class="py-docstring">'''</tt> </tt>
<a name="L352"></a><tt class="py-lineno">352</tt>  <tt class="py-line"><tt class="py-docstring">    Gradient search with momentum</tt> </tt>
<a name="L353"></a><tt class="py-lineno">353</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L354"></a><tt class="py-lineno">354</tt>  <tt class="py-line"><tt class="py-docstring">    This method uses the fact that the gradient of a function points to the</tt> </tt>
<a name="L355"></a><tt class="py-lineno">355</tt>  <tt class="py-line"><tt class="py-docstring">    direction of largest increase in the function (in general called *uphill*</tt> </tt>
<a name="L356"></a><tt class="py-lineno">356</tt>  <tt class="py-line"><tt class="py-docstring">    direction). So, the contrary direction (*downhill*) is used as search</tt> </tt>
<a name="L357"></a><tt class="py-lineno">357</tt>  <tt class="py-line"><tt class="py-docstring">    direction. A momentum term is added to avoid local minima.</tt> </tt>
<a name="L358"></a><tt class="py-lineno">358</tt>  <tt class="py-line"><tt class="py-docstring">    '''</tt> </tt>
<a name="MomentumGradient.__init__"></a><div id="MomentumGradient.__init__-def"><a name="L359"></a><tt class="py-lineno">359</tt> <a class="py-toggle" href="#" id="MomentumGradient.__init__-toggle" onclick="return toggle('MomentumGradient.__init__');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.MomentumGradient-class.html#__init__">__init__</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">f</tt><tt class="py-op">,</tt> <tt class="py-param">x0</tt><tt class="py-op">,</tt> <tt class="py-param">ranges</tt><tt class="py-op">=</tt><tt class="py-name">None</tt><tt class="py-op">,</tt> <tt class="py-param">df</tt><tt class="py-op">=</tt><tt class="py-name">None</tt><tt class="py-op">,</tt> <tt class="py-param">h</tt><tt class="py-op">=</tt><tt class="py-number">0.1</tt><tt class="py-op">,</tt> <tt class="py-param">a</tt><tt class="py-op">=</tt><tt class="py-number">0.1</tt><tt class="py-op">,</tt> <tt class="py-param">emax</tt><tt class="py-op">=</tt><tt class="py-number">1e-5</tt><tt class="py-op">,</tt> <tt class="py-param">imax</tt><tt class="py-op">=</tt><tt class="py-number">1000</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="MomentumGradient.__init__-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="MomentumGradient.__init__-expanded"><a name="L360"></a><tt class="py-lineno">360</tt>  <tt class="py-line">        <tt class="py-docstring">'''</tt> </tt>
<a name="L361"></a><tt class="py-lineno">361</tt>  <tt class="py-line"><tt class="py-docstring">        Initializes the optimizer.</tt> </tt>
<a name="L362"></a><tt class="py-lineno">362</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L363"></a><tt class="py-lineno">363</tt>  <tt class="py-line"><tt class="py-docstring">        To create an optimizer of this type, instantiate the class with the</tt> </tt>
<a name="L364"></a><tt class="py-lineno">364</tt>  <tt class="py-line"><tt class="py-docstring">        parameters given below:</tt> </tt>
<a name="L365"></a><tt class="py-lineno">365</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L366"></a><tt class="py-lineno">366</tt>  <tt class="py-line"><tt class="py-docstring">        :Parameters:</tt> </tt>
<a name="L367"></a><tt class="py-lineno">367</tt>  <tt class="py-line"><tt class="py-docstring">          f</tt> </tt>
<a name="L368"></a><tt class="py-lineno">368</tt>  <tt class="py-line"><tt class="py-docstring">            A multivariable function to be optimized. The function should have</tt> </tt>
<a name="L369"></a><tt class="py-lineno">369</tt>  <tt class="py-line"><tt class="py-docstring">            only one parameter, a multidimensional line-vector, and return the</tt> </tt>
<a name="L370"></a><tt class="py-lineno">370</tt>  <tt class="py-line"><tt class="py-docstring">            function value, a scalar.</tt> </tt>
<a name="L371"></a><tt class="py-lineno">371</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L372"></a><tt class="py-lineno">372</tt>  <tt class="py-line"><tt class="py-docstring">          x0</tt> </tt>
<a name="L373"></a><tt class="py-lineno">373</tt>  <tt class="py-line"><tt class="py-docstring">            First estimate of the minimum. Estimates can be given in any format,</tt> </tt>
<a name="L374"></a><tt class="py-lineno">374</tt>  <tt class="py-line"><tt class="py-docstring">            but internally they are converted to a one-dimension vector, where</tt> </tt>
<a name="L375"></a><tt class="py-lineno">375</tt>  <tt class="py-line"><tt class="py-docstring">            each component corresponds to the estimate of that particular</tt> </tt>
<a name="L376"></a><tt class="py-lineno">376</tt>  <tt class="py-line"><tt class="py-docstring">            variable. The vector is computed by flattening the array.</tt> </tt>
<a name="L377"></a><tt class="py-lineno">377</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L378"></a><tt class="py-lineno">378</tt>  <tt class="py-line"><tt class="py-docstring">          ranges</tt> </tt>
<a name="L379"></a><tt class="py-lineno">379</tt>  <tt class="py-line"><tt class="py-docstring">            A range of values might be passed to the algorithm, but it is not</tt> </tt>
<a name="L380"></a><tt class="py-lineno">380</tt>  <tt class="py-line"><tt class="py-docstring">            necessary. If supplied, this parameter should be a list of ranges</tt> </tt>
<a name="L381"></a><tt class="py-lineno">381</tt>  <tt class="py-line"><tt class="py-docstring">            for each variable of the objective function. It is specified as a</tt> </tt>
<a name="L382"></a><tt class="py-lineno">382</tt>  <tt class="py-line"><tt class="py-docstring">            list of tuples of two values, ``(x0, x1)``, where ``x0`` is the</tt> </tt>
<a name="L383"></a><tt class="py-lineno">383</tt>  <tt class="py-line"><tt class="py-docstring">            start of the interval, and ``x1`` its end. Obviously, ``x0`` should</tt> </tt>
<a name="L384"></a><tt class="py-lineno">384</tt>  <tt class="py-line"><tt class="py-docstring">            be smaller than ``x1``. It can also be given as a list with a simple</tt> </tt>
<a name="L385"></a><tt class="py-lineno">385</tt>  <tt class="py-line"><tt class="py-docstring">            tuple in the same format. In that case, the same range will be</tt> </tt>
<a name="L386"></a><tt class="py-lineno">386</tt>  <tt class="py-line"><tt class="py-docstring">            applied for every variable in the optimization.</tt> </tt>
<a name="L387"></a><tt class="py-lineno">387</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L388"></a><tt class="py-lineno">388</tt>  <tt class="py-line"><tt class="py-docstring">          df</tt> </tt>
<a name="L389"></a><tt class="py-lineno">389</tt>  <tt class="py-line"><tt class="py-docstring">            A function to calculate the gradient vector of the cost function</tt> </tt>
<a name="L390"></a><tt class="py-lineno">390</tt>  <tt class="py-line"><tt class="py-docstring">            ``f``. Defaults to ``None``, if no gradient is supplied, then it is</tt> </tt>
<a name="L391"></a><tt class="py-lineno">391</tt>  <tt class="py-line"><tt class="py-docstring">            estimated from the cost function using Euler equations.</tt> </tt>
<a name="L392"></a><tt class="py-lineno">392</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L393"></a><tt class="py-lineno">393</tt>  <tt class="py-line"><tt class="py-docstring">          h</tt> </tt>
<a name="L394"></a><tt class="py-lineno">394</tt>  <tt class="py-line"><tt class="py-docstring">            Convergence step. This method does not takes into consideration the</tt> </tt>
<a name="L395"></a><tt class="py-lineno">395</tt>  <tt class="py-line"><tt class="py-docstring">            possibility of varying the convergence step, to avoid Stiefel cages.</tt> </tt>
<a name="L396"></a><tt class="py-lineno">396</tt>  <tt class="py-line"><tt class="py-docstring">            Defaults to 0.1.</tt> </tt>
<a name="L397"></a><tt class="py-lineno">397</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L398"></a><tt class="py-lineno">398</tt>  <tt class="py-line"><tt class="py-docstring">          a</tt> </tt>
<a name="L399"></a><tt class="py-lineno">399</tt>  <tt class="py-line"><tt class="py-docstring">            Momentum term. This term is a measure of the memory of the optmizer.</tt> </tt>
<a name="L400"></a><tt class="py-lineno">400</tt>  <tt class="py-line"><tt class="py-docstring">            The bigger it is, the more the past values influence in the outcome</tt> </tt>
<a name="L401"></a><tt class="py-lineno">401</tt>  <tt class="py-line"><tt class="py-docstring">            of the optimization. Defaults to 0.1</tt> </tt>
<a name="L402"></a><tt class="py-lineno">402</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L403"></a><tt class="py-lineno">403</tt>  <tt class="py-line"><tt class="py-docstring">          emax</tt> </tt>
<a name="L404"></a><tt class="py-lineno">404</tt>  <tt class="py-line"><tt class="py-docstring">            Maximum allowed error. The algorithm stops as soon as the error is</tt> </tt>
<a name="L405"></a><tt class="py-lineno">405</tt>  <tt class="py-line"><tt class="py-docstring">            below this level. The error is absolute.</tt> </tt>
<a name="L406"></a><tt class="py-lineno">406</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L407"></a><tt class="py-lineno">407</tt>  <tt class="py-line"><tt class="py-docstring">          imax</tt> </tt>
<a name="L408"></a><tt class="py-lineno">408</tt>  <tt class="py-line"><tt class="py-docstring">            Maximum number of iterations, the algorithm stops as soon this</tt> </tt>
<a name="L409"></a><tt class="py-lineno">409</tt>  <tt class="py-line"><tt class="py-docstring">            number of iterations are executed, no matter what the error is at</tt> </tt>
<a name="L410"></a><tt class="py-lineno">410</tt>  <tt class="py-line"><tt class="py-docstring">            the moment.</tt> </tt>
<a name="L411"></a><tt class="py-lineno">411</tt>  <tt class="py-line"><tt class="py-docstring">        '''</tt> </tt>
<a name="L412"></a><tt class="py-lineno">412</tt>  <tt class="py-line">        <tt id="link-55" class="py-name"><a title="peach.optm.base.Optimizer" class="py-name" href="#" onclick="return doclink('link-55', 'Optimizer', 'link-3');">Optimizer</a></tt><tt class="py-op">.</tt><tt id="link-56" class="py-name"><a title="peach.fuzzy.base.FuzzySet.__init__
peach.fuzzy.cmeans.FuzzyCMeans.__init__
peach.fuzzy.control.Controller.__init__
peach.fuzzy.control.Parametric.__init__
peach.fuzzy.mf.Bell.__init__
peach.fuzzy.mf.DecreasingRamp.__init__
peach.fuzzy.mf.DecreasingSigmoid.__init__
peach.fuzzy.mf.Gaussian.__init__
peach.fuzzy.mf.IncreasingRamp.__init__
peach.fuzzy.mf.IncreasingSigmoid.__init__
peach.fuzzy.mf.Membership.__init__
peach.fuzzy.mf.RaisedCosine.__init__
peach.fuzzy.mf.Smf.__init__
peach.fuzzy.mf.Trapezoid.__init__
peach.fuzzy.mf.Triangle.__init__
peach.fuzzy.mf.Zmf.__init__
peach.ga.base.GeneticAlgorithm.__init__
peach.ga.chromosome.Chromosome.__init__
peach.ga.crossover.OnePoint.__init__
peach.ga.crossover.TwoPoint.__init__
peach.ga.crossover.Uniform.__init__
peach.ga.fitness.Fitness.__init__
peach.ga.fitness.Ranking.__init__
peach.ga.mutation.BitToBit.__init__
peach.nn.af.Activation.__init__
peach.nn.af.ArcTan.__init__
peach.nn.af.Gaussian.__init__
peach.nn.af.Linear.__init__
peach.nn.af.Ramp.__init__
peach.nn.af.Sigmoid.__init__
peach.nn.af.Signum.__init__
peach.nn.af.TanH.__init__
peach.nn.af.Threshold.__init__
peach.nn.base.Layer.__init__
peach.nn.kmeans.KMeans.__init__
peach.nn.lrules.BackPropagation.__init__
peach.nn.lrules.Competitive.__init__
peach.nn.lrules.Cooperative.__init__
peach.nn.lrules.LMS.__init__
peach.nn.lrules.WinnerTakesAll.__init__
peach.nn.mem.Hopfield.__init__
peach.nn.nnet.FeedForward.__init__
peach.nn.nnet.GRNN.__init__
peach.nn.nnet.PNN.__init__
peach.nn.nnet.SOM.__init__
peach.nn.rbfn.RBFN.__init__
peach.optm.base.Optimizer.__init__
peach.optm.linear.Direct1D.__init__
peach.optm.linear.Fibonacci.__init__
peach.optm.linear.GoldenRule.__init__
peach.optm.linear.Interpolation.__init__
peach.optm.multivar.Direct.__init__
peach.optm.multivar.Gradient.__init__
peach.optm.multivar.MomentumGradient.__init__
peach.optm.multivar.Newton.__init__
peach.optm.quasinewton.BFGS.__init__
peach.optm.quasinewton.DFP.__init__
peach.optm.quasinewton.SR1.__init__
peach.optm.stochastic.CrossEntropy.__init__
peach.pso.acc.Accelerator.__init__
peach.pso.acc.StandardPSO.__init__
peach.pso.base.ParticleSwarmOptimizer.__init__
peach.sa.base.BinarySA.__init__
peach.sa.base.ContinuousSA.__init__
peach.sa.neighbor.BinaryNeighbor.__init__
peach.sa.neighbor.ContinuousNeighbor.__init__
peach.sa.neighbor.GaussianNeighbor.__init__
peach.sa.neighbor.InvertBitsNeighbor.__init__
peach.sa.neighbor.UniformNeighbor.__init__" class="py-name" href="#" onclick="return doclink('link-56', '__init__', 'link-7');">__init__</a></tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">)</tt> </tt>
<a name="L413"></a><tt class="py-lineno">413</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__f</tt> <tt class="py-op">=</tt> <tt class="py-name">f</tt> </tt>
<a name="L414"></a><tt class="py-lineno">414</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt> <tt class="py-op">=</tt> <tt class="py-name">array</tt><tt class="py-op">(</tt><tt class="py-name">x0</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt class="py-name">ravel</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L415"></a><tt class="py-lineno">415</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__dx</tt> <tt class="py-op">=</tt> <tt class="py-name">zeros</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt><tt class="py-op">.</tt><tt id="link-57" class="py-name"><a title="peach.nn.base.Layer.shape" class="py-name" href="#" onclick="return doclink('link-57', 'shape', 'link-9');">shape</a></tt><tt class="py-op">)</tt> </tt>
<a name="L416"></a><tt class="py-lineno">416</tt>  <tt class="py-line">        <tt class="py-keyword">if</tt> <tt class="py-name">df</tt> <tt class="py-keyword">is</tt> <tt class="py-name">None</tt><tt class="py-op">:</tt> </tt>
<a name="L417"></a><tt class="py-lineno">417</tt>  <tt class="py-line">            <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__df</tt> <tt class="py-op">=</tt> <tt id="link-58" class="py-name"><a title="peach.optm.base.gradient" class="py-name" href="#" onclick="return doclink('link-58', 'gradient', 'link-4');">gradient</a></tt><tt class="py-op">(</tt><tt class="py-name">f</tt><tt class="py-op">)</tt> </tt>
<a name="L418"></a><tt class="py-lineno">418</tt>  <tt class="py-line">        <tt class="py-keyword">else</tt><tt class="py-op">:</tt> </tt>
<a name="L419"></a><tt class="py-lineno">419</tt>  <tt class="py-line">            <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__df</tt> <tt class="py-op">=</tt> <tt class="py-name">df</tt> </tt>
<a name="L420"></a><tt class="py-lineno">420</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__h</tt> <tt class="py-op">=</tt> <tt class="py-name">h</tt> </tt>
<a name="L421"></a><tt class="py-lineno">421</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__a</tt> <tt class="py-op">=</tt> <tt class="py-name">a</tt> </tt>
<a name="L422"></a><tt class="py-lineno">422</tt>  <tt class="py-line"> </tt>
<a name="L423"></a><tt class="py-lineno">423</tt>  <tt class="py-line">        <tt class="py-comment"># Determine ranges of the variables</tt> </tt>
<a name="L424"></a><tt class="py-lineno">424</tt>  <tt class="py-line">        <tt class="py-keyword">if</tt> <tt class="py-name">ranges</tt> <tt class="py-keyword">is</tt> <tt class="py-keyword">not</tt> <tt class="py-name">None</tt><tt class="py-op">:</tt> </tt>
<a name="L425"></a><tt class="py-lineno">425</tt>  <tt class="py-line">            <tt class="py-name">ranges</tt> <tt class="py-op">=</tt> <tt class="py-name">list</tt><tt class="py-op">(</tt><tt class="py-name">ranges</tt><tt class="py-op">)</tt> </tt>
<a name="L426"></a><tt class="py-lineno">426</tt>  <tt class="py-line">            <tt class="py-keyword">if</tt> <tt class="py-name">len</tt><tt class="py-op">(</tt><tt class="py-name">ranges</tt><tt class="py-op">)</tt> <tt class="py-op">==</tt> <tt class="py-number">1</tt><tt class="py-op">:</tt> </tt>
<a name="L427"></a><tt class="py-lineno">427</tt>  <tt class="py-line">                <tt class="py-name">ranges</tt> <tt class="py-op">=</tt> <tt class="py-name">array</tt><tt class="py-op">(</tt><tt class="py-name">ranges</tt> <tt class="py-op">*</tt> <tt class="py-name">len</tt><tt class="py-op">(</tt><tt class="py-name">x0</tt><tt class="py-op">[</tt><tt class="py-number">0</tt><tt class="py-op">]</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
<a name="L428"></a><tt class="py-lineno">428</tt>  <tt class="py-line">            <tt class="py-keyword">else</tt><tt class="py-op">:</tt> </tt>
<a name="L429"></a><tt class="py-lineno">429</tt>  <tt class="py-line">                <tt class="py-name">ranges</tt> <tt class="py-op">=</tt> <tt class="py-name">array</tt><tt class="py-op">(</tt><tt class="py-name">ranges</tt><tt class="py-op">)</tt> </tt>
<a name="L430"></a><tt class="py-lineno">430</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">ranges</tt> <tt class="py-op">=</tt> <tt class="py-name">ranges</tt> </tt>
<a name="L431"></a><tt class="py-lineno">431</tt>  <tt class="py-line">        <tt class="py-string">'''Holds the ranges for every variable. Although it is a writable</tt> </tt>
<a name="L432"></a><tt class="py-lineno">432</tt>  <tt class="py-line"><tt class="py-string">        property, care should be taken in changing parameters before ending the</tt> </tt>
<a name="L433"></a><tt class="py-lineno">433</tt>  <tt class="py-line"><tt class="py-string">        convergence.'''</tt> </tt>
<a name="L434"></a><tt class="py-lineno">434</tt>  <tt class="py-line"> </tt>
<a name="L435"></a><tt class="py-lineno">435</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__emax</tt> <tt class="py-op">=</tt> <tt class="py-name">float</tt><tt class="py-op">(</tt><tt class="py-name">emax</tt><tt class="py-op">)</tt> </tt>
<a name="L436"></a><tt class="py-lineno">436</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__imax</tt> <tt class="py-op">=</tt> <tt class="py-name">int</tt><tt class="py-op">(</tt><tt class="py-name">imax</tt><tt class="py-op">)</tt> </tt>
</div><a name="L437"></a><tt class="py-lineno">437</tt>  <tt class="py-line"> </tt>
<a name="L438"></a><tt class="py-lineno">438</tt>  <tt class="py-line"> </tt>
<a name="MomentumGradient.__get_x"></a><div id="MomentumGradient.__get_x-def"><a name="L439"></a><tt class="py-lineno">439</tt> <a class="py-toggle" href="#" id="MomentumGradient.__get_x-toggle" onclick="return toggle('MomentumGradient.__get_x');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.MomentumGradient-class.html#__get_x">__get_x</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="MomentumGradient.__get_x-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="MomentumGradient.__get_x-expanded"><a name="L440"></a><tt class="py-lineno">440</tt>  <tt class="py-line">        <tt class="py-keyword">return</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt> </tt>
</div><a name="L441"></a><tt class="py-lineno">441</tt>  <tt class="py-line"> </tt>
<a name="MomentumGradient.__set_x"></a><div id="MomentumGradient.__set_x-def"><a name="L442"></a><tt class="py-lineno">442</tt> <a class="py-toggle" href="#" id="MomentumGradient.__set_x-toggle" onclick="return toggle('MomentumGradient.__set_x');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.MomentumGradient-class.html#__set_x">__set_x</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">x0</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="MomentumGradient.__set_x-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="MomentumGradient.__set_x-expanded"><a name="L443"></a><tt class="py-lineno">443</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt id="link-59" class="py-name"><a title="peach.ga.base.GeneticAlgorithm.restart
peach.optm.linear.Direct1D.restart
peach.optm.linear.Fibonacci.restart
peach.optm.linear.GoldenRule.restart
peach.optm.linear.Interpolation.restart
peach.optm.multivar.Direct.restart
peach.optm.multivar.Gradient.restart
peach.optm.multivar.MomentumGradient.restart
peach.optm.multivar.Newton.restart
peach.optm.quasinewton.BFGS.restart
peach.optm.quasinewton.DFP.restart
peach.optm.quasinewton.SR1.restart
peach.pso.base.ParticleSwarmOptimizer.restart
peach.sa.base.BinarySA.restart
peach.sa.base.ContinuousSA.restart" class="py-name" href="#" onclick="return doclink('link-59', 'restart', 'link-10');">restart</a></tt><tt class="py-op">(</tt><tt class="py-name">x0</tt><tt class="py-op">)</tt> </tt>
</div><a name="L444"></a><tt class="py-lineno">444</tt>  <tt class="py-line"> </tt>
<a name="L445"></a><tt class="py-lineno">445</tt>  <tt class="py-line">    <tt id="link-60" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-60', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt class="py-name">property</tt><tt class="py-op">(</tt><tt id="link-61" class="py-name"><a title="peach.optm.linear.Direct1D.__get_x
peach.optm.linear.GoldenRule.__get_x
peach.optm.linear.Interpolation.__get_x
peach.optm.multivar.Direct.__get_x
peach.optm.multivar.Gradient.__get_x
peach.optm.multivar.MomentumGradient.__get_x
peach.optm.multivar.Newton.__get_x
peach.optm.quasinewton.DFP.__get_x
peach.optm.quasinewton.SR1.__get_x
peach.sa.base.BinarySA.__get_x
peach.sa.base.ContinuousSA.__get_x" class="py-name" href="#" onclick="return doclink('link-61', '__get_x', 'link-12');">__get_x</a></tt><tt class="py-op">,</tt> <tt id="link-62" class="py-name"><a title="peach.optm.linear.Direct1D.__set_x
peach.optm.linear.GoldenRule.__set_x
peach.optm.linear.Interpolation.__set_x
peach.optm.multivar.Direct.__set_x
peach.optm.multivar.Gradient.__set_x
peach.optm.multivar.MomentumGradient.__set_x
peach.optm.multivar.Newton.__set_x
peach.optm.quasinewton.DFP.__set_x
peach.optm.quasinewton.SR1.__set_x
peach.sa.base.BinarySA.__set_x
peach.sa.base.ContinuousSA.__set_x" class="py-name" href="#" onclick="return doclink('link-62', '__set_x', 'link-13');">__set_x</a></tt><tt class="py-op">)</tt> </tt>
<a name="L446"></a><tt class="py-lineno">446</tt>  <tt class="py-line">    <tt class="py-string">'''The estimate of the position of the minimum.'''</tt> </tt>
<a name="L447"></a><tt class="py-lineno">447</tt>  <tt class="py-line"> </tt>
<a name="L448"></a><tt class="py-lineno">448</tt>  <tt class="py-line"> </tt>
<a name="MomentumGradient.restart"></a><div id="MomentumGradient.restart-def"><a name="L449"></a><tt class="py-lineno">449</tt> <a class="py-toggle" href="#" id="MomentumGradient.restart-toggle" onclick="return toggle('MomentumGradient.restart');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.MomentumGradient-class.html#restart">restart</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">x0</tt><tt class="py-op">,</tt> <tt class="py-param">h</tt><tt class="py-op">=</tt><tt class="py-name">None</tt><tt class="py-op">,</tt> <tt class="py-param">a</tt><tt class="py-op">=</tt><tt class="py-name">None</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="MomentumGradient.restart-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="MomentumGradient.restart-expanded"><a name="L450"></a><tt class="py-lineno">450</tt>  <tt class="py-line">        <tt class="py-docstring">'''</tt> </tt>
<a name="L451"></a><tt class="py-lineno">451</tt>  <tt class="py-line"><tt class="py-docstring">        Resets the optimizer, returning to its original state, and allowing to</tt> </tt>
<a name="L452"></a><tt class="py-lineno">452</tt>  <tt class="py-line"><tt class="py-docstring">        use a new first estimate.</tt> </tt>
<a name="L453"></a><tt class="py-lineno">453</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L454"></a><tt class="py-lineno">454</tt>  <tt class="py-line"><tt class="py-docstring">        :Parameters:</tt> </tt>
<a name="L455"></a><tt class="py-lineno">455</tt>  <tt class="py-line"><tt class="py-docstring">          x0</tt> </tt>
<a name="L456"></a><tt class="py-lineno">456</tt>  <tt class="py-line"><tt class="py-docstring">            New estimate of the minimum. Estimates can be given in any format,</tt> </tt>
<a name="L457"></a><tt class="py-lineno">457</tt>  <tt class="py-line"><tt class="py-docstring">            but internally they are converted to a one-dimension vector, where</tt> </tt>
<a name="L458"></a><tt class="py-lineno">458</tt>  <tt class="py-line"><tt class="py-docstring">            each component corresponds to the estimate of that particular</tt> </tt>
<a name="L459"></a><tt class="py-lineno">459</tt>  <tt class="py-line"><tt class="py-docstring">            variable. The vector is computed by flattening the array.</tt> </tt>
<a name="L460"></a><tt class="py-lineno">460</tt>  <tt class="py-line"><tt class="py-docstring">          h</tt> </tt>
<a name="L461"></a><tt class="py-lineno">461</tt>  <tt class="py-line"><tt class="py-docstring">            Convergence step. This method does not takes into consideration the</tt> </tt>
<a name="L462"></a><tt class="py-lineno">462</tt>  <tt class="py-line"><tt class="py-docstring">            possibility of varying the convergence step, to avoid Stiefel cages.</tt> </tt>
<a name="L463"></a><tt class="py-lineno">463</tt>  <tt class="py-line"><tt class="py-docstring">            If not given in this method, the old value is used.</tt> </tt>
<a name="L464"></a><tt class="py-lineno">464</tt>  <tt class="py-line"><tt class="py-docstring">          a</tt> </tt>
<a name="L465"></a><tt class="py-lineno">465</tt>  <tt class="py-line"><tt class="py-docstring">            Momentum term. This term is a measure of the memory of the optmizer.</tt> </tt>
<a name="L466"></a><tt class="py-lineno">466</tt>  <tt class="py-line"><tt class="py-docstring">            The bigger it is, the more the past values influence in the outcome</tt> </tt>
<a name="L467"></a><tt class="py-lineno">467</tt>  <tt class="py-line"><tt class="py-docstring">            of the optimization. If not given in this method, the old value is</tt> </tt>
<a name="L468"></a><tt class="py-lineno">468</tt>  <tt class="py-line"><tt class="py-docstring">            used.</tt> </tt>
<a name="L469"></a><tt class="py-lineno">469</tt>  <tt class="py-line"><tt class="py-docstring">        '''</tt> </tt>
<a name="L470"></a><tt class="py-lineno">470</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt> <tt class="py-op">=</tt> <tt class="py-name">array</tt><tt class="py-op">(</tt><tt class="py-name">x0</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt class="py-name">ravel</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L471"></a><tt class="py-lineno">471</tt>  <tt class="py-line">        <tt class="py-keyword">if</tt> <tt class="py-name">h</tt> <tt class="py-keyword">is</tt> <tt class="py-keyword">not</tt> <tt class="py-name">None</tt><tt class="py-op">:</tt> </tt>
<a name="L472"></a><tt class="py-lineno">472</tt>  <tt class="py-line">            <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__h</tt> <tt class="py-op">=</tt> <tt class="py-name">h</tt> </tt>
<a name="L473"></a><tt class="py-lineno">473</tt>  <tt class="py-line">        <tt class="py-keyword">if</tt> <tt class="py-name">a</tt> <tt class="py-keyword">is</tt> <tt class="py-keyword">not</tt> <tt class="py-name">None</tt><tt class="py-op">:</tt> </tt>
<a name="L474"></a><tt class="py-lineno">474</tt>  <tt class="py-line">            <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__a</tt> <tt class="py-op">=</tt> <tt class="py-name">a</tt> </tt>
</div><a name="L475"></a><tt class="py-lineno">475</tt>  <tt class="py-line"> </tt>
<a name="L476"></a><tt class="py-lineno">476</tt>  <tt class="py-line"> </tt>
<a name="MomentumGradient.step"></a><div id="MomentumGradient.step-def"><a name="L477"></a><tt class="py-lineno">477</tt> <a class="py-toggle" href="#" id="MomentumGradient.step-toggle" onclick="return toggle('MomentumGradient.step');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.MomentumGradient-class.html#step">step</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="MomentumGradient.step-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="MomentumGradient.step-expanded"><a name="L478"></a><tt class="py-lineno">478</tt>  <tt class="py-line">        <tt class="py-docstring">'''</tt> </tt>
<a name="L479"></a><tt class="py-lineno">479</tt>  <tt class="py-line"><tt class="py-docstring">        One step of the search.</tt> </tt>
<a name="L480"></a><tt class="py-lineno">480</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L481"></a><tt class="py-lineno">481</tt>  <tt class="py-line"><tt class="py-docstring">        In this method, the result of the step is dependent only of the given</tt> </tt>
<a name="L482"></a><tt class="py-lineno">482</tt>  <tt class="py-line"><tt class="py-docstring">        estimated, so it can be used for different kind of investigations on the</tt> </tt>
<a name="L483"></a><tt class="py-lineno">483</tt>  <tt class="py-line"><tt class="py-docstring">        same cost function.</tt> </tt>
<a name="L484"></a><tt class="py-lineno">484</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L485"></a><tt class="py-lineno">485</tt>  <tt class="py-line"><tt class="py-docstring">        :Returns:</tt> </tt>
<a name="L486"></a><tt class="py-lineno">486</tt>  <tt class="py-line"><tt class="py-docstring">          This method returns a tuple ``(x, e)``, where ``x`` is the updated</tt> </tt>
<a name="L487"></a><tt class="py-lineno">487</tt>  <tt class="py-line"><tt class="py-docstring">          estimate of the minimum, and ``e`` is the estimated error.</tt> </tt>
<a name="L488"></a><tt class="py-lineno">488</tt>  <tt class="py-line"><tt class="py-docstring">        '''</tt> </tt>
<a name="L489"></a><tt class="py-lineno">489</tt>  <tt class="py-line">        <tt id="link-63" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-63', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt> </tt>
<a name="L490"></a><tt class="py-lineno">490</tt>  <tt class="py-line">        <tt class="py-name">xold</tt> <tt class="py-op">=</tt> <tt id="link-64" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-64', 'x', 'link-11');">x</a></tt> </tt>
<a name="L491"></a><tt class="py-lineno">491</tt>  <tt class="py-line"> </tt>
<a name="L492"></a><tt class="py-lineno">492</tt>  <tt class="py-line">        <tt class="py-comment"># New estimate</tt> </tt>
<a name="L493"></a><tt class="py-lineno">493</tt>  <tt class="py-line">        <tt class="py-name">dx</tt> <tt class="py-op">=</tt> <tt class="py-op">-</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__h</tt> <tt class="py-op">*</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__df</tt><tt class="py-op">(</tt><tt id="link-65" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-65', 'x', 'link-11');">x</a></tt><tt class="py-op">)</tt> <tt class="py-op">+</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__a</tt> <tt class="py-op">*</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__dx</tt> </tt>
<a name="L494"></a><tt class="py-lineno">494</tt>  <tt class="py-line">        <tt id="link-66" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-66', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt id="link-67" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-67', 'x', 'link-11');">x</a></tt> <tt class="py-op">+</tt> <tt class="py-name">dx</tt> </tt>
<a name="L495"></a><tt class="py-lineno">495</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__dx</tt> <tt class="py-op">=</tt> <tt class="py-name">dx</tt> </tt>
<a name="L496"></a><tt class="py-lineno">496</tt>  <tt class="py-line"> </tt>
<a name="L497"></a><tt class="py-lineno">497</tt>  <tt class="py-line">        <tt class="py-comment"># Sanity check</tt> </tt>
<a name="L498"></a><tt class="py-lineno">498</tt>  <tt class="py-line">        <tt class="py-keyword">if</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">ranges</tt> <tt class="py-keyword">is</tt> <tt class="py-keyword">not</tt> <tt class="py-name">None</tt><tt class="py-op">:</tt> </tt>
<a name="L499"></a><tt class="py-lineno">499</tt>  <tt class="py-line">            <tt class="py-name">r0</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">ranges</tt><tt class="py-op">[</tt><tt class="py-op">:</tt><tt class="py-op">,</tt> <tt class="py-number">0</tt><tt class="py-op">]</tt> </tt>
<a name="L500"></a><tt class="py-lineno">500</tt>  <tt class="py-line">            <tt class="py-name">r1</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">ranges</tt><tt class="py-op">[</tt><tt class="py-op">:</tt><tt class="py-op">,</tt> <tt class="py-number">1</tt><tt class="py-op">]</tt> </tt>
<a name="L501"></a><tt class="py-lineno">501</tt>  <tt class="py-line">            <tt id="link-68" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-68', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt class="py-name">where</tt><tt class="py-op">(</tt><tt id="link-69" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-69', 'x', 'link-11');">x</a></tt> <tt class="py-op">&lt;</tt> <tt class="py-name">r0</tt><tt class="py-op">,</tt> <tt class="py-name">r0</tt><tt class="py-op">,</tt> <tt id="link-70" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-70', 'x', 'link-11');">x</a></tt><tt class="py-op">)</tt> </tt>
<a name="L502"></a><tt class="py-lineno">502</tt>  <tt class="py-line">            <tt id="link-71" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-71', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt class="py-name">where</tt><tt class="py-op">(</tt><tt id="link-72" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-72', 'x', 'link-11');">x</a></tt> <tt class="py-op">&gt;</tt> <tt class="py-name">r1</tt><tt class="py-op">,</tt> <tt class="py-name">r1</tt><tt class="py-op">,</tt> <tt id="link-73" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-73', 'x', 'link-11');">x</a></tt><tt class="py-op">)</tt> </tt>
<a name="L503"></a><tt class="py-lineno">503</tt>  <tt class="py-line"> </tt>
<a name="L504"></a><tt class="py-lineno">504</tt>  <tt class="py-line">        <tt class="py-comment"># Update state</tt> </tt>
<a name="L505"></a><tt class="py-lineno">505</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt> <tt class="py-op">=</tt> <tt id="link-74" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-74', 'x', 'link-11');">x</a></tt> </tt>
<a name="L506"></a><tt class="py-lineno">506</tt>  <tt class="py-line">        <tt class="py-keyword">return</tt> <tt id="link-75" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-75', 'x', 'link-11');">x</a></tt><tt class="py-op">,</tt> <tt class="py-name">sum</tt><tt class="py-op">(</tt><tt id="link-76" class="py-name"><a title="peach.nn.rbfn.abs
peach.pso.base.abs" class="py-name" href="#" onclick="return doclink('link-76', 'abs', 'link-1');">abs</a></tt><tt class="py-op">(</tt><tt id="link-77" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-77', 'x', 'link-11');">x</a></tt> <tt class="py-op">-</tt> <tt class="py-name">xold</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
</div><a name="L507"></a><tt class="py-lineno">507</tt>  <tt class="py-line"> </tt>
<a name="L508"></a><tt class="py-lineno">508</tt>  <tt class="py-line"> </tt>
<a name="MomentumGradient.__call__"></a><div id="MomentumGradient.__call__-def"><a name="L509"></a><tt class="py-lineno">509</tt> <a class="py-toggle" href="#" id="MomentumGradient.__call__-toggle" onclick="return toggle('MomentumGradient.__call__');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.MomentumGradient-class.html#__call__">__call__</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="MomentumGradient.__call__-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="MomentumGradient.__call__-expanded"><a name="L510"></a><tt class="py-lineno">510</tt>  <tt class="py-line">        <tt class="py-docstring">'''</tt> </tt>
<a name="L511"></a><tt class="py-lineno">511</tt>  <tt class="py-line"><tt class="py-docstring">        Transparently executes the search until the minimum is found. The stop</tt> </tt>
<a name="L512"></a><tt class="py-lineno">512</tt>  <tt class="py-line"><tt class="py-docstring">        criteria are the maximum error or the maximum number of iterations,</tt> </tt>
<a name="L513"></a><tt class="py-lineno">513</tt>  <tt class="py-line"><tt class="py-docstring">        whichever is reached first. Note that this is a ``__call__`` method, so</tt> </tt>
<a name="L514"></a><tt class="py-lineno">514</tt>  <tt class="py-line"><tt class="py-docstring">        the object is called as a function. This method returns a tuple</tt> </tt>
<a name="L515"></a><tt class="py-lineno">515</tt>  <tt class="py-line"><tt class="py-docstring">        ``(x, e)``, with the best estimate of the minimum and the error.</tt> </tt>
<a name="L516"></a><tt class="py-lineno">516</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L517"></a><tt class="py-lineno">517</tt>  <tt class="py-line"><tt class="py-docstring">        :Returns:</tt> </tt>
<a name="L518"></a><tt class="py-lineno">518</tt>  <tt class="py-line"><tt class="py-docstring">          This method returns a tuple ``(x, e)``, where ``x`` is the best</tt> </tt>
<a name="L519"></a><tt class="py-lineno">519</tt>  <tt class="py-line"><tt class="py-docstring">          estimate of the minimum, and ``e`` is the estimated error.</tt> </tt>
<a name="L520"></a><tt class="py-lineno">520</tt>  <tt class="py-line"><tt class="py-docstring">        '''</tt> </tt>
<a name="L521"></a><tt class="py-lineno">521</tt>  <tt class="py-line">        <tt class="py-name">emax</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__emax</tt> </tt>
<a name="L522"></a><tt class="py-lineno">522</tt>  <tt class="py-line">        <tt class="py-name">imax</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__imax</tt> </tt>
<a name="L523"></a><tt class="py-lineno">523</tt>  <tt class="py-line">        <tt class="py-name">e</tt> <tt class="py-op">=</tt> <tt class="py-name">emax</tt> </tt>
<a name="L524"></a><tt class="py-lineno">524</tt>  <tt class="py-line">        <tt class="py-name">i</tt> <tt class="py-op">=</tt> <tt class="py-number">0</tt> </tt>
<a name="L525"></a><tt class="py-lineno">525</tt>  <tt class="py-line">        <tt class="py-keyword">while</tt> <tt class="py-name">e</tt> <tt class="py-op">&gt;</tt> <tt class="py-name">emax</tt><tt class="py-op">/</tt><tt class="py-number">2.</tt> <tt class="py-keyword">and</tt> <tt class="py-name">i</tt> <tt class="py-op">&lt;</tt> <tt class="py-name">imax</tt><tt class="py-op">:</tt> </tt>
<a name="L526"></a><tt class="py-lineno">526</tt>  <tt class="py-line">            <tt class="py-name">_</tt><tt class="py-op">,</tt> <tt class="py-name">e</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt id="link-78" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.step
peach.ga.base.GeneticAlgorithm.step
peach.nn.kmeans.KMeans.step
peach.nn.mem.Hopfield.step
peach.optm.base.Optimizer.step
peach.optm.linear.Direct1D.step
peach.optm.linear.Fibonacci.step
peach.optm.linear.GoldenRule.step
peach.optm.linear.Interpolation.step
peach.optm.multivar.Direct.step
peach.optm.multivar.Gradient.step
peach.optm.multivar.MomentumGradient.step
peach.optm.multivar.Newton.step
peach.optm.quasinewton.BFGS.step
peach.optm.quasinewton.DFP.step
peach.optm.quasinewton.SR1.step
peach.optm.stochastic.CrossEntropy.step
peach.pso.base.ParticleSwarmOptimizer.step
peach.sa.base.BinarySA.step
peach.sa.base.ContinuousSA.step" class="py-name" href="#" onclick="return doclink('link-78', 'step', 'link-31');">step</a></tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L527"></a><tt class="py-lineno">527</tt>  <tt class="py-line">            <tt class="py-name">i</tt> <tt class="py-op">=</tt> <tt class="py-name">i</tt> <tt class="py-op">+</tt> <tt class="py-number">1</tt> </tt>
<a name="L528"></a><tt class="py-lineno">528</tt>  <tt class="py-line">        <tt class="py-keyword">return</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt><tt class="py-op">,</tt> <tt class="py-name">e</tt> </tt>
</div></div><a name="L529"></a><tt class="py-lineno">529</tt>  <tt class="py-line"> </tt>
<a name="L530"></a><tt class="py-lineno">530</tt>  <tt class="py-line"> </tt>
<a name="L531"></a><tt class="py-lineno">531</tt>  <tt class="py-line"><tt class="py-comment">################################################################################</tt> </tt>
<a name="Newton"></a><div id="Newton-def"><a name="L532"></a><tt class="py-lineno">532</tt> <a class="py-toggle" href="#" id="Newton-toggle" onclick="return toggle('Newton');">-</a><tt class="py-line"><tt class="py-keyword">class</tt> <a class="py-def-name" href="peach.optm.multivar.Newton-class.html">Newton</a><tt class="py-op">(</tt><tt class="py-base-class">Optimizer</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Newton-collapsed" style="display:none;" pad="+++" indent="++++"></div><div id="Newton-expanded"><a name="L533"></a><tt class="py-lineno">533</tt>  <tt class="py-line">    <tt class="py-docstring">'''</tt> </tt>
<a name="L534"></a><tt class="py-lineno">534</tt>  <tt class="py-line"><tt class="py-docstring">    Newton search</tt> </tt>
<a name="L535"></a><tt class="py-lineno">535</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L536"></a><tt class="py-lineno">536</tt>  <tt class="py-line"><tt class="py-docstring">    This is a very effective method to find minimum points in functions. In a</tt> </tt>
<a name="L537"></a><tt class="py-lineno">537</tt>  <tt class="py-line"><tt class="py-docstring">    very basic fashion, this method corresponds to using Newton root finding</tt> </tt>
<a name="L538"></a><tt class="py-lineno">538</tt>  <tt class="py-line"><tt class="py-docstring">    method on f'(x). Converges *very* fast if the cost function is quadratic</tt> </tt>
<a name="L539"></a><tt class="py-lineno">539</tt>  <tt class="py-line"><tt class="py-docstring">    of similar to it.</tt> </tt>
<a name="L540"></a><tt class="py-lineno">540</tt>  <tt class="py-line"><tt class="py-docstring">    '''</tt> </tt>
<a name="Newton.__init__"></a><div id="Newton.__init__-def"><a name="L541"></a><tt class="py-lineno">541</tt> <a class="py-toggle" href="#" id="Newton.__init__-toggle" onclick="return toggle('Newton.__init__');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.Newton-class.html#__init__">__init__</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">f</tt><tt class="py-op">,</tt> <tt class="py-param">x0</tt><tt class="py-op">,</tt> <tt class="py-param">ranges</tt><tt class="py-op">=</tt><tt class="py-name">None</tt><tt class="py-op">,</tt> <tt class="py-param">df</tt><tt class="py-op">=</tt><tt class="py-name">None</tt><tt class="py-op">,</tt> <tt class="py-param">hf</tt><tt class="py-op">=</tt><tt class="py-name">None</tt><tt class="py-op">,</tt> <tt class="py-param">h</tt><tt class="py-op">=</tt><tt class="py-number">0.1</tt><tt class="py-op">,</tt> <tt class="py-param">emax</tt><tt class="py-op">=</tt><tt class="py-number">1e-5</tt><tt class="py-op">,</tt> <tt class="py-param">imax</tt><tt class="py-op">=</tt><tt class="py-number">1000</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Newton.__init__-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="Newton.__init__-expanded"><a name="L542"></a><tt class="py-lineno">542</tt>  <tt class="py-line">        <tt class="py-docstring">'''</tt> </tt>
<a name="L543"></a><tt class="py-lineno">543</tt>  <tt class="py-line"><tt class="py-docstring">        Initializes the optimizer.</tt> </tt>
<a name="L544"></a><tt class="py-lineno">544</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L545"></a><tt class="py-lineno">545</tt>  <tt class="py-line"><tt class="py-docstring">        To create an optimizer of this type, instantiate the class with the</tt> </tt>
<a name="L546"></a><tt class="py-lineno">546</tt>  <tt class="py-line"><tt class="py-docstring">        parameters given below:</tt> </tt>
<a name="L547"></a><tt class="py-lineno">547</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L548"></a><tt class="py-lineno">548</tt>  <tt class="py-line"><tt class="py-docstring">        :Parameters:</tt> </tt>
<a name="L549"></a><tt class="py-lineno">549</tt>  <tt class="py-line"><tt class="py-docstring">          f</tt> </tt>
<a name="L550"></a><tt class="py-lineno">550</tt>  <tt class="py-line"><tt class="py-docstring">            A multivariable function to be optimized. The function should have</tt> </tt>
<a name="L551"></a><tt class="py-lineno">551</tt>  <tt class="py-line"><tt class="py-docstring">            only one parameter, a multidimensional line-vector, and return the</tt> </tt>
<a name="L552"></a><tt class="py-lineno">552</tt>  <tt class="py-line"><tt class="py-docstring">            function value, a scalar.</tt> </tt>
<a name="L553"></a><tt class="py-lineno">553</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L554"></a><tt class="py-lineno">554</tt>  <tt class="py-line"><tt class="py-docstring">          x0</tt> </tt>
<a name="L555"></a><tt class="py-lineno">555</tt>  <tt class="py-line"><tt class="py-docstring">            First estimate of the minimum. Estimates can be given in any format,</tt> </tt>
<a name="L556"></a><tt class="py-lineno">556</tt>  <tt class="py-line"><tt class="py-docstring">            but internally they are converted to a one-dimension vector, where</tt> </tt>
<a name="L557"></a><tt class="py-lineno">557</tt>  <tt class="py-line"><tt class="py-docstring">            each component corresponds to the estimate of that particular</tt> </tt>
<a name="L558"></a><tt class="py-lineno">558</tt>  <tt class="py-line"><tt class="py-docstring">            variable. The vector is computed by flattening the array.</tt> </tt>
<a name="L559"></a><tt class="py-lineno">559</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L560"></a><tt class="py-lineno">560</tt>  <tt class="py-line"><tt class="py-docstring">          ranges</tt> </tt>
<a name="L561"></a><tt class="py-lineno">561</tt>  <tt class="py-line"><tt class="py-docstring">            A range of values might be passed to the algorithm, but it is not</tt> </tt>
<a name="L562"></a><tt class="py-lineno">562</tt>  <tt class="py-line"><tt class="py-docstring">            necessary. If supplied, this parameter should be a list of ranges</tt> </tt>
<a name="L563"></a><tt class="py-lineno">563</tt>  <tt class="py-line"><tt class="py-docstring">            for each variable of the objective function. It is specified as a</tt> </tt>
<a name="L564"></a><tt class="py-lineno">564</tt>  <tt class="py-line"><tt class="py-docstring">            list of tuples of two values, ``(x0, x1)``, where ``x0`` is the</tt> </tt>
<a name="L565"></a><tt class="py-lineno">565</tt>  <tt class="py-line"><tt class="py-docstring">            start of the interval, and ``x1`` its end. Obviously, ``x0`` should</tt> </tt>
<a name="L566"></a><tt class="py-lineno">566</tt>  <tt class="py-line"><tt class="py-docstring">            be smaller than ``x1``. It can also be given as a list with a simple</tt> </tt>
<a name="L567"></a><tt class="py-lineno">567</tt>  <tt class="py-line"><tt class="py-docstring">            tuple in the same format. In that case, the same range will be</tt> </tt>
<a name="L568"></a><tt class="py-lineno">568</tt>  <tt class="py-line"><tt class="py-docstring">            applied for every variable in the optimization.</tt> </tt>
<a name="L569"></a><tt class="py-lineno">569</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L570"></a><tt class="py-lineno">570</tt>  <tt class="py-line"><tt class="py-docstring">          df</tt> </tt>
<a name="L571"></a><tt class="py-lineno">571</tt>  <tt class="py-line"><tt class="py-docstring">            A function to calculate the gradient vector of the cost function</tt> </tt>
<a name="L572"></a><tt class="py-lineno">572</tt>  <tt class="py-line"><tt class="py-docstring">            ``f``. Defaults to ``None``, if no gradient is supplied, then it is</tt> </tt>
<a name="L573"></a><tt class="py-lineno">573</tt>  <tt class="py-line"><tt class="py-docstring">            estimated from the cost function using Euler equations.</tt> </tt>
<a name="L574"></a><tt class="py-lineno">574</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L575"></a><tt class="py-lineno">575</tt>  <tt class="py-line"><tt class="py-docstring">          hf</tt> </tt>
<a name="L576"></a><tt class="py-lineno">576</tt>  <tt class="py-line"><tt class="py-docstring">            A function to calculate the hessian matrix of the cost function</tt> </tt>
<a name="L577"></a><tt class="py-lineno">577</tt>  <tt class="py-line"><tt class="py-docstring">            ``f``. Defaults to ``None``, if no hessian is supplied, then it is</tt> </tt>
<a name="L578"></a><tt class="py-lineno">578</tt>  <tt class="py-line"><tt class="py-docstring">            estimated from the cost function using Euler equations.</tt> </tt>
<a name="L579"></a><tt class="py-lineno">579</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L580"></a><tt class="py-lineno">580</tt>  <tt class="py-line"><tt class="py-docstring">          h</tt> </tt>
<a name="L581"></a><tt class="py-lineno">581</tt>  <tt class="py-line"><tt class="py-docstring">            Convergence step. This method does not takes into consideration the</tt> </tt>
<a name="L582"></a><tt class="py-lineno">582</tt>  <tt class="py-line"><tt class="py-docstring">            possibility of varying the convergence step, to avoid Stiefel cages.</tt> </tt>
<a name="L583"></a><tt class="py-lineno">583</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L584"></a><tt class="py-lineno">584</tt>  <tt class="py-line"><tt class="py-docstring">          emax</tt> </tt>
<a name="L585"></a><tt class="py-lineno">585</tt>  <tt class="py-line"><tt class="py-docstring">            Maximum allowed error. The algorithm stops as soon as the error is</tt> </tt>
<a name="L586"></a><tt class="py-lineno">586</tt>  <tt class="py-line"><tt class="py-docstring">            below this level. The error is absolute.</tt> </tt>
<a name="L587"></a><tt class="py-lineno">587</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L588"></a><tt class="py-lineno">588</tt>  <tt class="py-line"><tt class="py-docstring">          imax</tt> </tt>
<a name="L589"></a><tt class="py-lineno">589</tt>  <tt class="py-line"><tt class="py-docstring">            Maximum number of iterations, the algorithm stops as soon this</tt> </tt>
<a name="L590"></a><tt class="py-lineno">590</tt>  <tt class="py-line"><tt class="py-docstring">            number of iterations are executed, no matter what the error is at</tt> </tt>
<a name="L591"></a><tt class="py-lineno">591</tt>  <tt class="py-line"><tt class="py-docstring">            the moment.</tt> </tt>
<a name="L592"></a><tt class="py-lineno">592</tt>  <tt class="py-line"><tt class="py-docstring">        '''</tt> </tt>
<a name="L593"></a><tt class="py-lineno">593</tt>  <tt class="py-line">        <tt id="link-79" class="py-name"><a title="peach.optm.base.Optimizer" class="py-name" href="#" onclick="return doclink('link-79', 'Optimizer', 'link-3');">Optimizer</a></tt><tt class="py-op">.</tt><tt id="link-80" class="py-name"><a title="peach.fuzzy.base.FuzzySet.__init__
peach.fuzzy.cmeans.FuzzyCMeans.__init__
peach.fuzzy.control.Controller.__init__
peach.fuzzy.control.Parametric.__init__
peach.fuzzy.mf.Bell.__init__
peach.fuzzy.mf.DecreasingRamp.__init__
peach.fuzzy.mf.DecreasingSigmoid.__init__
peach.fuzzy.mf.Gaussian.__init__
peach.fuzzy.mf.IncreasingRamp.__init__
peach.fuzzy.mf.IncreasingSigmoid.__init__
peach.fuzzy.mf.Membership.__init__
peach.fuzzy.mf.RaisedCosine.__init__
peach.fuzzy.mf.Smf.__init__
peach.fuzzy.mf.Trapezoid.__init__
peach.fuzzy.mf.Triangle.__init__
peach.fuzzy.mf.Zmf.__init__
peach.ga.base.GeneticAlgorithm.__init__
peach.ga.chromosome.Chromosome.__init__
peach.ga.crossover.OnePoint.__init__
peach.ga.crossover.TwoPoint.__init__
peach.ga.crossover.Uniform.__init__
peach.ga.fitness.Fitness.__init__
peach.ga.fitness.Ranking.__init__
peach.ga.mutation.BitToBit.__init__
peach.nn.af.Activation.__init__
peach.nn.af.ArcTan.__init__
peach.nn.af.Gaussian.__init__
peach.nn.af.Linear.__init__
peach.nn.af.Ramp.__init__
peach.nn.af.Sigmoid.__init__
peach.nn.af.Signum.__init__
peach.nn.af.TanH.__init__
peach.nn.af.Threshold.__init__
peach.nn.base.Layer.__init__
peach.nn.kmeans.KMeans.__init__
peach.nn.lrules.BackPropagation.__init__
peach.nn.lrules.Competitive.__init__
peach.nn.lrules.Cooperative.__init__
peach.nn.lrules.LMS.__init__
peach.nn.lrules.WinnerTakesAll.__init__
peach.nn.mem.Hopfield.__init__
peach.nn.nnet.FeedForward.__init__
peach.nn.nnet.GRNN.__init__
peach.nn.nnet.PNN.__init__
peach.nn.nnet.SOM.__init__
peach.nn.rbfn.RBFN.__init__
peach.optm.base.Optimizer.__init__
peach.optm.linear.Direct1D.__init__
peach.optm.linear.Fibonacci.__init__
peach.optm.linear.GoldenRule.__init__
peach.optm.linear.Interpolation.__init__
peach.optm.multivar.Direct.__init__
peach.optm.multivar.Gradient.__init__
peach.optm.multivar.MomentumGradient.__init__
peach.optm.multivar.Newton.__init__
peach.optm.quasinewton.BFGS.__init__
peach.optm.quasinewton.DFP.__init__
peach.optm.quasinewton.SR1.__init__
peach.optm.stochastic.CrossEntropy.__init__
peach.pso.acc.Accelerator.__init__
peach.pso.acc.StandardPSO.__init__
peach.pso.base.ParticleSwarmOptimizer.__init__
peach.sa.base.BinarySA.__init__
peach.sa.base.ContinuousSA.__init__
peach.sa.neighbor.BinaryNeighbor.__init__
peach.sa.neighbor.ContinuousNeighbor.__init__
peach.sa.neighbor.GaussianNeighbor.__init__
peach.sa.neighbor.InvertBitsNeighbor.__init__
peach.sa.neighbor.UniformNeighbor.__init__" class="py-name" href="#" onclick="return doclink('link-80', '__init__', 'link-7');">__init__</a></tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">)</tt> </tt>
<a name="L594"></a><tt class="py-lineno">594</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__f</tt> <tt class="py-op">=</tt> <tt class="py-name">f</tt> </tt>
<a name="L595"></a><tt class="py-lineno">595</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt> <tt class="py-op">=</tt> <tt class="py-name">array</tt><tt class="py-op">(</tt><tt class="py-name">x0</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt class="py-name">ravel</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L596"></a><tt class="py-lineno">596</tt>  <tt class="py-line">        <tt class="py-keyword">if</tt> <tt class="py-name">df</tt> <tt class="py-keyword">is</tt> <tt class="py-name">None</tt><tt class="py-op">:</tt> </tt>
<a name="L597"></a><tt class="py-lineno">597</tt>  <tt class="py-line">            <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__df</tt> <tt class="py-op">=</tt> <tt id="link-81" class="py-name"><a title="peach.optm.base.gradient" class="py-name" href="#" onclick="return doclink('link-81', 'gradient', 'link-4');">gradient</a></tt><tt class="py-op">(</tt><tt class="py-name">f</tt><tt class="py-op">)</tt> </tt>
<a name="L598"></a><tt class="py-lineno">598</tt>  <tt class="py-line">        <tt class="py-keyword">else</tt><tt class="py-op">:</tt> </tt>
<a name="L599"></a><tt class="py-lineno">599</tt>  <tt class="py-line">            <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__df</tt> <tt class="py-op">=</tt> <tt class="py-name">df</tt> </tt>
<a name="L600"></a><tt class="py-lineno">600</tt>  <tt class="py-line">        <tt class="py-keyword">if</tt> <tt class="py-name">hf</tt> <tt class="py-keyword">is</tt> <tt class="py-name">None</tt><tt class="py-op">:</tt> </tt>
<a name="L601"></a><tt class="py-lineno">601</tt>  <tt class="py-line">            <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__hf</tt> <tt class="py-op">=</tt> <tt id="link-82" class="py-name"><a title="peach.optm.base.hessian" class="py-name" href="#" onclick="return doclink('link-82', 'hessian', 'link-5');">hessian</a></tt><tt class="py-op">(</tt><tt class="py-name">f</tt><tt class="py-op">)</tt> </tt>
<a name="L602"></a><tt class="py-lineno">602</tt>  <tt class="py-line">        <tt class="py-keyword">else</tt><tt class="py-op">:</tt> </tt>
<a name="L603"></a><tt class="py-lineno">603</tt>  <tt class="py-line">            <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__hf</tt> <tt class="py-op">=</tt> <tt class="py-name">hf</tt> </tt>
<a name="L604"></a><tt class="py-lineno">604</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__h</tt> <tt class="py-op">=</tt> <tt class="py-name">h</tt> </tt>
<a name="L605"></a><tt class="py-lineno">605</tt>  <tt class="py-line"> </tt>
<a name="L606"></a><tt class="py-lineno">606</tt>  <tt class="py-line">        <tt class="py-comment"># Determine ranges of the variables</tt> </tt>
<a name="L607"></a><tt class="py-lineno">607</tt>  <tt class="py-line">        <tt class="py-keyword">if</tt> <tt class="py-name">ranges</tt> <tt class="py-keyword">is</tt> <tt class="py-keyword">not</tt> <tt class="py-name">None</tt><tt class="py-op">:</tt> </tt>
<a name="L608"></a><tt class="py-lineno">608</tt>  <tt class="py-line">            <tt class="py-name">ranges</tt> <tt class="py-op">=</tt> <tt class="py-name">list</tt><tt class="py-op">(</tt><tt class="py-name">ranges</tt><tt class="py-op">)</tt> </tt>
<a name="L609"></a><tt class="py-lineno">609</tt>  <tt class="py-line">            <tt class="py-keyword">if</tt> <tt class="py-name">len</tt><tt class="py-op">(</tt><tt class="py-name">ranges</tt><tt class="py-op">)</tt> <tt class="py-op">==</tt> <tt class="py-number">1</tt><tt class="py-op">:</tt> </tt>
<a name="L610"></a><tt class="py-lineno">610</tt>  <tt class="py-line">                <tt class="py-name">ranges</tt> <tt class="py-op">=</tt> <tt class="py-name">array</tt><tt class="py-op">(</tt><tt class="py-name">ranges</tt> <tt class="py-op">*</tt> <tt class="py-name">len</tt><tt class="py-op">(</tt><tt class="py-name">x0</tt><tt class="py-op">[</tt><tt class="py-number">0</tt><tt class="py-op">]</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
<a name="L611"></a><tt class="py-lineno">611</tt>  <tt class="py-line">            <tt class="py-keyword">else</tt><tt class="py-op">:</tt> </tt>
<a name="L612"></a><tt class="py-lineno">612</tt>  <tt class="py-line">                <tt class="py-name">ranges</tt> <tt class="py-op">=</tt> <tt class="py-name">array</tt><tt class="py-op">(</tt><tt class="py-name">ranges</tt><tt class="py-op">)</tt> </tt>
<a name="L613"></a><tt class="py-lineno">613</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">ranges</tt> <tt class="py-op">=</tt> <tt class="py-name">ranges</tt> </tt>
<a name="L614"></a><tt class="py-lineno">614</tt>  <tt class="py-line">        <tt class="py-string">'''Holds the ranges for every variable. Although it is a writable</tt> </tt>
<a name="L615"></a><tt class="py-lineno">615</tt>  <tt class="py-line"><tt class="py-string">        property, care should be taken in changing parameters before ending the</tt> </tt>
<a name="L616"></a><tt class="py-lineno">616</tt>  <tt class="py-line"><tt class="py-string">        convergence.'''</tt> </tt>
<a name="L617"></a><tt class="py-lineno">617</tt>  <tt class="py-line"> </tt>
<a name="L618"></a><tt class="py-lineno">618</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__emax</tt> <tt class="py-op">=</tt> <tt class="py-name">float</tt><tt class="py-op">(</tt><tt class="py-name">emax</tt><tt class="py-op">)</tt> </tt>
<a name="L619"></a><tt class="py-lineno">619</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__imax</tt> <tt class="py-op">=</tt> <tt class="py-name">int</tt><tt class="py-op">(</tt><tt class="py-name">imax</tt><tt class="py-op">)</tt> </tt>
</div><a name="L620"></a><tt class="py-lineno">620</tt>  <tt class="py-line"> </tt>
<a name="L621"></a><tt class="py-lineno">621</tt>  <tt class="py-line"> </tt>
<a name="Newton.__get_x"></a><div id="Newton.__get_x-def"><a name="L622"></a><tt class="py-lineno">622</tt> <a class="py-toggle" href="#" id="Newton.__get_x-toggle" onclick="return toggle('Newton.__get_x');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.Newton-class.html#__get_x">__get_x</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Newton.__get_x-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="Newton.__get_x-expanded"><a name="L623"></a><tt class="py-lineno">623</tt>  <tt class="py-line">        <tt class="py-keyword">return</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt> </tt>
</div><a name="L624"></a><tt class="py-lineno">624</tt>  <tt class="py-line"> </tt>
<a name="Newton.__set_x"></a><div id="Newton.__set_x-def"><a name="L625"></a><tt class="py-lineno">625</tt> <a class="py-toggle" href="#" id="Newton.__set_x-toggle" onclick="return toggle('Newton.__set_x');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.Newton-class.html#__set_x">__set_x</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">x0</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Newton.__set_x-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="Newton.__set_x-expanded"><a name="L626"></a><tt class="py-lineno">626</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt id="link-83" class="py-name"><a title="peach.ga.base.GeneticAlgorithm.restart
peach.optm.linear.Direct1D.restart
peach.optm.linear.Fibonacci.restart
peach.optm.linear.GoldenRule.restart
peach.optm.linear.Interpolation.restart
peach.optm.multivar.Direct.restart
peach.optm.multivar.Gradient.restart
peach.optm.multivar.MomentumGradient.restart
peach.optm.multivar.Newton.restart
peach.optm.quasinewton.BFGS.restart
peach.optm.quasinewton.DFP.restart
peach.optm.quasinewton.SR1.restart
peach.pso.base.ParticleSwarmOptimizer.restart
peach.sa.base.BinarySA.restart
peach.sa.base.ContinuousSA.restart" class="py-name" href="#" onclick="return doclink('link-83', 'restart', 'link-10');">restart</a></tt><tt class="py-op">(</tt><tt class="py-name">x0</tt><tt class="py-op">)</tt> </tt>
</div><a name="L627"></a><tt class="py-lineno">627</tt>  <tt class="py-line"> </tt>
<a name="L628"></a><tt class="py-lineno">628</tt>  <tt class="py-line">    <tt id="link-84" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-84', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt class="py-name">property</tt><tt class="py-op">(</tt><tt id="link-85" class="py-name"><a title="peach.optm.linear.Direct1D.__get_x
peach.optm.linear.GoldenRule.__get_x
peach.optm.linear.Interpolation.__get_x
peach.optm.multivar.Direct.__get_x
peach.optm.multivar.Gradient.__get_x
peach.optm.multivar.MomentumGradient.__get_x
peach.optm.multivar.Newton.__get_x
peach.optm.quasinewton.DFP.__get_x
peach.optm.quasinewton.SR1.__get_x
peach.sa.base.BinarySA.__get_x
peach.sa.base.ContinuousSA.__get_x" class="py-name" href="#" onclick="return doclink('link-85', '__get_x', 'link-12');">__get_x</a></tt><tt class="py-op">,</tt> <tt id="link-86" class="py-name"><a title="peach.optm.linear.Direct1D.__set_x
peach.optm.linear.GoldenRule.__set_x
peach.optm.linear.Interpolation.__set_x
peach.optm.multivar.Direct.__set_x
peach.optm.multivar.Gradient.__set_x
peach.optm.multivar.MomentumGradient.__set_x
peach.optm.multivar.Newton.__set_x
peach.optm.quasinewton.DFP.__set_x
peach.optm.quasinewton.SR1.__set_x
peach.sa.base.BinarySA.__set_x
peach.sa.base.ContinuousSA.__set_x" class="py-name" href="#" onclick="return doclink('link-86', '__set_x', 'link-13');">__set_x</a></tt><tt class="py-op">)</tt> </tt>
<a name="L629"></a><tt class="py-lineno">629</tt>  <tt class="py-line">    <tt class="py-string">'''The estimate of the position of the minimum.'''</tt> </tt>
<a name="L630"></a><tt class="py-lineno">630</tt>  <tt class="py-line"> </tt>
<a name="L631"></a><tt class="py-lineno">631</tt>  <tt class="py-line"> </tt>
<a name="Newton.restart"></a><div id="Newton.restart-def"><a name="L632"></a><tt class="py-lineno">632</tt> <a class="py-toggle" href="#" id="Newton.restart-toggle" onclick="return toggle('Newton.restart');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.Newton-class.html#restart">restart</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">x0</tt><tt class="py-op">,</tt> <tt class="py-param">h</tt><tt class="py-op">=</tt><tt class="py-name">None</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Newton.restart-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="Newton.restart-expanded"><a name="L633"></a><tt class="py-lineno">633</tt>  <tt class="py-line">        <tt class="py-docstring">'''</tt> </tt>
<a name="L634"></a><tt class="py-lineno">634</tt>  <tt class="py-line"><tt class="py-docstring">        Resets the optimizer, returning to its original state, and allowing to</tt> </tt>
<a name="L635"></a><tt class="py-lineno">635</tt>  <tt class="py-line"><tt class="py-docstring">        use a new first estimate.</tt> </tt>
<a name="L636"></a><tt class="py-lineno">636</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L637"></a><tt class="py-lineno">637</tt>  <tt class="py-line"><tt class="py-docstring">        :Parameters:</tt> </tt>
<a name="L638"></a><tt class="py-lineno">638</tt>  <tt class="py-line"><tt class="py-docstring">          x0</tt> </tt>
<a name="L639"></a><tt class="py-lineno">639</tt>  <tt class="py-line"><tt class="py-docstring">            New estimate of the minimum. Estimates can be given in any format,</tt> </tt>
<a name="L640"></a><tt class="py-lineno">640</tt>  <tt class="py-line"><tt class="py-docstring">            but internally they are converted to a one-dimension vector, where</tt> </tt>
<a name="L641"></a><tt class="py-lineno">641</tt>  <tt class="py-line"><tt class="py-docstring">            each component corresponds to the estimate of that particular</tt> </tt>
<a name="L642"></a><tt class="py-lineno">642</tt>  <tt class="py-line"><tt class="py-docstring">            variable. The vector is computed by flattening the array.</tt> </tt>
<a name="L643"></a><tt class="py-lineno">643</tt>  <tt class="py-line"><tt class="py-docstring">          h</tt> </tt>
<a name="L644"></a><tt class="py-lineno">644</tt>  <tt class="py-line"><tt class="py-docstring">            Convergence step. This method does not takes into consideration the</tt> </tt>
<a name="L645"></a><tt class="py-lineno">645</tt>  <tt class="py-line"><tt class="py-docstring">            possibility of varying the convergence step, to avoid Stiefel cages.</tt> </tt>
<a name="L646"></a><tt class="py-lineno">646</tt>  <tt class="py-line"><tt class="py-docstring">        '''</tt> </tt>
<a name="L647"></a><tt class="py-lineno">647</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt> <tt class="py-op">=</tt> <tt class="py-name">array</tt><tt class="py-op">(</tt><tt class="py-name">x0</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt class="py-name">ravel</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L648"></a><tt class="py-lineno">648</tt>  <tt class="py-line">        <tt class="py-keyword">if</tt> <tt class="py-name">h</tt> <tt class="py-keyword">is</tt> <tt class="py-keyword">not</tt> <tt class="py-name">None</tt><tt class="py-op">:</tt> </tt>
<a name="L649"></a><tt class="py-lineno">649</tt>  <tt class="py-line">            <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__h</tt> <tt class="py-op">=</tt> <tt class="py-name">h</tt> </tt>
</div><a name="L650"></a><tt class="py-lineno">650</tt>  <tt class="py-line"> </tt>
<a name="L651"></a><tt class="py-lineno">651</tt>  <tt class="py-line"> </tt>
<a name="Newton.step"></a><div id="Newton.step-def"><a name="L652"></a><tt class="py-lineno">652</tt> <a class="py-toggle" href="#" id="Newton.step-toggle" onclick="return toggle('Newton.step');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.Newton-class.html#step">step</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Newton.step-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="Newton.step-expanded"><a name="L653"></a><tt class="py-lineno">653</tt>  <tt class="py-line">        <tt class="py-docstring">'''</tt> </tt>
<a name="L654"></a><tt class="py-lineno">654</tt>  <tt class="py-line"><tt class="py-docstring">        One step of the search.</tt> </tt>
<a name="L655"></a><tt class="py-lineno">655</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L656"></a><tt class="py-lineno">656</tt>  <tt class="py-line"><tt class="py-docstring">        In this method, the result of the step is dependent only of the given</tt> </tt>
<a name="L657"></a><tt class="py-lineno">657</tt>  <tt class="py-line"><tt class="py-docstring">        estimated, so it can be used for different kind of investigations on the</tt> </tt>
<a name="L658"></a><tt class="py-lineno">658</tt>  <tt class="py-line"><tt class="py-docstring">        same cost function.</tt> </tt>
<a name="L659"></a><tt class="py-lineno">659</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L660"></a><tt class="py-lineno">660</tt>  <tt class="py-line"><tt class="py-docstring">        :Returns:</tt> </tt>
<a name="L661"></a><tt class="py-lineno">661</tt>  <tt class="py-line"><tt class="py-docstring">          This method returns a tuple ``(x, e)``, where ``x`` is the updated</tt> </tt>
<a name="L662"></a><tt class="py-lineno">662</tt>  <tt class="py-line"><tt class="py-docstring">          estimate of the minimum, and ``e`` is the estimated error.</tt> </tt>
<a name="L663"></a><tt class="py-lineno">663</tt>  <tt class="py-line"><tt class="py-docstring">        '''</tt> </tt>
<a name="L664"></a><tt class="py-lineno">664</tt>  <tt class="py-line">        <tt id="link-87" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-87', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt> </tt>
<a name="L665"></a><tt class="py-lineno">665</tt>  <tt class="py-line">        <tt class="py-name">xold</tt> <tt class="py-op">=</tt> <tt id="link-88" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-88', 'x', 'link-11');">x</a></tt> </tt>
<a name="L666"></a><tt class="py-lineno">666</tt>  <tt class="py-line">        <tt class="py-name">df</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__df</tt><tt class="py-op">(</tt><tt id="link-89" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-89', 'x', 'link-11');">x</a></tt><tt class="py-op">)</tt> </tt>
<a name="L667"></a><tt class="py-lineno">667</tt>  <tt class="py-line">        <tt class="py-name">hf</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__hf</tt><tt class="py-op">(</tt><tt id="link-90" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-90', 'x', 'link-11');">x</a></tt><tt class="py-op">)</tt> </tt>
<a name="L668"></a><tt class="py-lineno">668</tt>  <tt class="py-line"> </tt>
<a name="L669"></a><tt class="py-lineno">669</tt>  <tt class="py-line">        <tt class="py-comment"># New estimate</tt> </tt>
<a name="L670"></a><tt class="py-lineno">670</tt>  <tt class="py-line">        <tt class="py-keyword">try</tt><tt class="py-op">:</tt> </tt>
<a name="L671"></a><tt class="py-lineno">671</tt>  <tt class="py-line">            <tt id="link-91" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-91', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt id="link-92" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-92', 'x', 'link-11');">x</a></tt> <tt class="py-op">-</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__h</tt> <tt class="py-op">*</tt> <tt class="py-name">dot</tt><tt class="py-op">(</tt><tt class="py-name">inv</tt><tt class="py-op">(</tt><tt class="py-name">hf</tt><tt class="py-op">)</tt><tt class="py-op">,</tt> <tt class="py-name">df</tt><tt class="py-op">)</tt> </tt>
<a name="L672"></a><tt class="py-lineno">672</tt>  <tt class="py-line">        <tt class="py-keyword">except</tt><tt class="py-op">:</tt> </tt>
<a name="L673"></a><tt class="py-lineno">673</tt>  <tt class="py-line">            <tt id="link-93" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-93', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt id="link-94" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-94', 'x', 'link-11');">x</a></tt> <tt class="py-op">-</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__h</tt> <tt class="py-op">*</tt> <tt class="py-name">df</tt> <tt class="py-op">/</tt> <tt class="py-name">hf</tt> </tt>
<a name="L674"></a><tt class="py-lineno">674</tt>  <tt class="py-line"> </tt>
<a name="L675"></a><tt class="py-lineno">675</tt>  <tt class="py-line">        <tt class="py-comment"># Sanity check</tt> </tt>
<a name="L676"></a><tt class="py-lineno">676</tt>  <tt class="py-line">        <tt class="py-keyword">if</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">ranges</tt> <tt class="py-keyword">is</tt> <tt class="py-keyword">not</tt> <tt class="py-name">None</tt><tt class="py-op">:</tt> </tt>
<a name="L677"></a><tt class="py-lineno">677</tt>  <tt class="py-line">            <tt class="py-name">r0</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">ranges</tt><tt class="py-op">[</tt><tt class="py-op">:</tt><tt class="py-op">,</tt> <tt class="py-number">0</tt><tt class="py-op">]</tt> </tt>
<a name="L678"></a><tt class="py-lineno">678</tt>  <tt class="py-line">            <tt class="py-name">r1</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">ranges</tt><tt class="py-op">[</tt><tt class="py-op">:</tt><tt class="py-op">,</tt> <tt class="py-number">1</tt><tt class="py-op">]</tt> </tt>
<a name="L679"></a><tt class="py-lineno">679</tt>  <tt class="py-line">            <tt id="link-95" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-95', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt class="py-name">where</tt><tt class="py-op">(</tt><tt id="link-96" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-96', 'x', 'link-11');">x</a></tt> <tt class="py-op">&lt;</tt> <tt class="py-name">r0</tt><tt class="py-op">,</tt> <tt class="py-name">r0</tt><tt class="py-op">,</tt> <tt id="link-97" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-97', 'x', 'link-11');">x</a></tt><tt class="py-op">)</tt> </tt>
<a name="L680"></a><tt class="py-lineno">680</tt>  <tt class="py-line">            <tt id="link-98" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-98', 'x', 'link-11');">x</a></tt> <tt class="py-op">=</tt> <tt class="py-name">where</tt><tt class="py-op">(</tt><tt id="link-99" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-99', 'x', 'link-11');">x</a></tt> <tt class="py-op">&gt;</tt> <tt class="py-name">r1</tt><tt class="py-op">,</tt> <tt class="py-name">r1</tt><tt class="py-op">,</tt> <tt id="link-100" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-100', 'x', 'link-11');">x</a></tt><tt class="py-op">)</tt> </tt>
<a name="L681"></a><tt class="py-lineno">681</tt>  <tt class="py-line"> </tt>
<a name="L682"></a><tt class="py-lineno">682</tt>  <tt class="py-line">        <tt class="py-comment"># Update state</tt> </tt>
<a name="L683"></a><tt class="py-lineno">683</tt>  <tt class="py-line">        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt> <tt class="py-op">=</tt> <tt id="link-101" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-101', 'x', 'link-11');">x</a></tt> </tt>
<a name="L684"></a><tt class="py-lineno">684</tt>  <tt class="py-line">        <tt class="py-keyword">return</tt> <tt id="link-102" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-102', 'x', 'link-11');">x</a></tt><tt class="py-op">,</tt> <tt class="py-name">sum</tt><tt class="py-op">(</tt><tt id="link-103" class="py-name"><a title="peach.nn.rbfn.abs
peach.pso.base.abs" class="py-name" href="#" onclick="return doclink('link-103', 'abs', 'link-1');">abs</a></tt><tt class="py-op">(</tt><tt id="link-104" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-104', 'x', 'link-11');">x</a></tt> <tt class="py-op">-</tt> <tt class="py-name">xold</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
</div><a name="L685"></a><tt class="py-lineno">685</tt>  <tt class="py-line"> </tt>
<a name="L686"></a><tt class="py-lineno">686</tt>  <tt class="py-line"> </tt>
<a name="Newton.__call__"></a><div id="Newton.__call__-def"><a name="L687"></a><tt class="py-lineno">687</tt> <a class="py-toggle" href="#" id="Newton.__call__-toggle" onclick="return toggle('Newton.__call__');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar.Newton-class.html#__call__">__call__</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="Newton.__call__-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="Newton.__call__-expanded"><a name="L688"></a><tt class="py-lineno">688</tt>  <tt class="py-line">        <tt class="py-docstring">'''</tt> </tt>
<a name="L689"></a><tt class="py-lineno">689</tt>  <tt class="py-line"><tt class="py-docstring">        Transparently executes the search until the minimum is found. The stop</tt> </tt>
<a name="L690"></a><tt class="py-lineno">690</tt>  <tt class="py-line"><tt class="py-docstring">        criteria are the maximum error or the maximum number of iterations,</tt> </tt>
<a name="L691"></a><tt class="py-lineno">691</tt>  <tt class="py-line"><tt class="py-docstring">        whichever is reached first. Note that this is a ``__call__`` method, so</tt> </tt>
<a name="L692"></a><tt class="py-lineno">692</tt>  <tt class="py-line"><tt class="py-docstring">        the object is called as a function. This method returns a tuple</tt> </tt>
<a name="L693"></a><tt class="py-lineno">693</tt>  <tt class="py-line"><tt class="py-docstring">        ``(x, e)``, with the best estimate of the minimum and the error.</tt> </tt>
<a name="L694"></a><tt class="py-lineno">694</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L695"></a><tt class="py-lineno">695</tt>  <tt class="py-line"><tt class="py-docstring">        :Returns:</tt> </tt>
<a name="L696"></a><tt class="py-lineno">696</tt>  <tt class="py-line"><tt class="py-docstring">          This method returns a tuple ``(x, e)``, where ``x`` is the best</tt> </tt>
<a name="L697"></a><tt class="py-lineno">697</tt>  <tt class="py-line"><tt class="py-docstring">          estimate of the minimum, and ``e`` is the estimated error.</tt> </tt>
<a name="L698"></a><tt class="py-lineno">698</tt>  <tt class="py-line"><tt class="py-docstring">        '''</tt> </tt>
<a name="L699"></a><tt class="py-lineno">699</tt>  <tt class="py-line">        <tt class="py-name">emax</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__emax</tt> </tt>
<a name="L700"></a><tt class="py-lineno">700</tt>  <tt class="py-line">        <tt class="py-name">imax</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__imax</tt> </tt>
<a name="L701"></a><tt class="py-lineno">701</tt>  <tt class="py-line">        <tt class="py-name">e</tt> <tt class="py-op">=</tt> <tt class="py-name">emax</tt> </tt>
<a name="L702"></a><tt class="py-lineno">702</tt>  <tt class="py-line">        <tt class="py-name">i</tt> <tt class="py-op">=</tt> <tt class="py-number">0</tt> </tt>
<a name="L703"></a><tt class="py-lineno">703</tt>  <tt class="py-line">        <tt class="py-keyword">while</tt> <tt class="py-name">e</tt> <tt class="py-op">&gt;</tt> <tt class="py-name">emax</tt><tt class="py-op">/</tt><tt class="py-number">2.</tt> <tt class="py-keyword">and</tt> <tt class="py-name">i</tt> <tt class="py-op">&lt;</tt> <tt class="py-name">imax</tt><tt class="py-op">:</tt> </tt>
<a name="L704"></a><tt class="py-lineno">704</tt>  <tt class="py-line">            <tt class="py-name">_</tt><tt class="py-op">,</tt> <tt class="py-name">e</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt id="link-105" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.step
peach.ga.base.GeneticAlgorithm.step
peach.nn.kmeans.KMeans.step
peach.nn.mem.Hopfield.step
peach.optm.base.Optimizer.step
peach.optm.linear.Direct1D.step
peach.optm.linear.Fibonacci.step
peach.optm.linear.GoldenRule.step
peach.optm.linear.Interpolation.step
peach.optm.multivar.Direct.step
peach.optm.multivar.Gradient.step
peach.optm.multivar.MomentumGradient.step
peach.optm.multivar.Newton.step
peach.optm.quasinewton.BFGS.step
peach.optm.quasinewton.DFP.step
peach.optm.quasinewton.SR1.step
peach.optm.stochastic.CrossEntropy.step
peach.pso.base.ParticleSwarmOptimizer.step
peach.sa.base.BinarySA.step
peach.sa.base.ContinuousSA.step" class="py-name" href="#" onclick="return doclink('link-105', 'step', 'link-31');">step</a></tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L705"></a><tt class="py-lineno">705</tt>  <tt class="py-line">            <tt class="py-name">i</tt> <tt class="py-op">=</tt> <tt class="py-name">i</tt> <tt class="py-op">+</tt> <tt class="py-number">1</tt> </tt>
<a name="L706"></a><tt class="py-lineno">706</tt>  <tt class="py-line">        <tt class="py-keyword">return</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__x</tt><tt class="py-op">,</tt> <tt class="py-name">e</tt> </tt>
</div></div><a name="L707"></a><tt class="py-lineno">707</tt>  <tt class="py-line"> </tt>
<a name="L708"></a><tt class="py-lineno">708</tt>  <tt class="py-line"> </tt>
<a name="L709"></a><tt class="py-lineno">709</tt>  <tt class="py-line"><tt class="py-comment">################################################################################</tt> </tt>
<a name="L710"></a><tt class="py-lineno">710</tt>  <tt class="py-line"><tt class="py-comment"># Test</tt> </tt>
<a name="L711"></a><tt class="py-lineno">711</tt>  <tt class="py-line"><tt class="py-keyword">if</tt> <tt class="py-name">__name__</tt> <tt class="py-op">==</tt> <tt class="py-string">"__main__"</tt><tt class="py-op">:</tt> </tt>
<a name="L712"></a><tt class="py-lineno">712</tt>  <tt class="py-line"> </tt>
<a name="L713"></a><tt class="py-lineno">713</tt>  <tt class="py-line">    <tt class="py-comment"># Rosenbrock function</tt> </tt>
<a name="f"></a><div id="f-def"><a name="L714"></a><tt class="py-lineno">714</tt> <a class="py-toggle" href="#" id="f-toggle" onclick="return toggle('f');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar-module.html#f">f</a><tt class="py-op">(</tt><tt class="py-param">xy</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="f-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="f-expanded"><a name="L715"></a><tt class="py-lineno">715</tt>  <tt class="py-line">        <tt id="link-106" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-106', 'x', 'link-11');">x</a></tt><tt class="py-op">,</tt> <tt id="link-107" class="py-name" targets="Variable peach.fuzzy.control.Controller.y=peach.fuzzy.control.Controller-class.html#y,Variable peach.nn.base.Layer.y=peach.nn.base.Layer-class.html#y,Variable peach.nn.nnet.FeedForward.y=peach.nn.nnet.FeedForward-class.html#y,Variable peach.nn.nnet.SOM.y=peach.nn.nnet.SOM-class.html#y,Variable peach.nn.rbfn.RBFN.y=peach.nn.rbfn.RBFN-class.html#y"><a title="peach.fuzzy.control.Controller.y
peach.nn.base.Layer.y
peach.nn.nnet.FeedForward.y
peach.nn.nnet.SOM.y
peach.nn.rbfn.RBFN.y" class="py-name" href="#" onclick="return doclink('link-107', 'y', 'link-107');">y</a></tt> <tt class="py-op">=</tt> <tt class="py-name">xy</tt> </tt>
<a name="L716"></a><tt class="py-lineno">716</tt>  <tt class="py-line">        <tt class="py-keyword">return</tt> <tt class="py-op">(</tt><tt class="py-number">1.</tt><tt class="py-op">-</tt><tt id="link-108" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-108', 'x', 'link-11');">x</a></tt><tt class="py-op">)</tt><tt class="py-op">**</tt><tt class="py-number">2.</tt> <tt class="py-op">+</tt> <tt class="py-op">(</tt><tt id="link-109" class="py-name"><a title="peach.fuzzy.control.Controller.y
peach.nn.base.Layer.y
peach.nn.nnet.FeedForward.y
peach.nn.nnet.SOM.y
peach.nn.rbfn.RBFN.y" class="py-name" href="#" onclick="return doclink('link-109', 'y', 'link-107');">y</a></tt><tt class="py-op">-</tt><tt id="link-110" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-110', 'x', 'link-11');">x</a></tt><tt class="py-op">*</tt><tt id="link-111" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-111', 'x', 'link-11');">x</a></tt><tt class="py-op">)</tt><tt class="py-op">**</tt><tt class="py-number">2.</tt> </tt>
</div><a name="L717"></a><tt class="py-lineno">717</tt>  <tt class="py-line"> </tt>
<a name="L718"></a><tt class="py-lineno">718</tt>  <tt class="py-line">    <tt class="py-comment"># Gradient of Rosenbrock function</tt> </tt>
<a name="df"></a><div id="df-def"><a name="L719"></a><tt class="py-lineno">719</tt> <a class="py-toggle" href="#" id="df-toggle" onclick="return toggle('df');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar-module.html#df">df</a><tt class="py-op">(</tt><tt class="py-param">xy</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="df-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="df-expanded"><a name="L720"></a><tt class="py-lineno">720</tt>  <tt class="py-line">        <tt id="link-112" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-112', 'x', 'link-11');">x</a></tt><tt class="py-op">,</tt> <tt id="link-113" class="py-name"><a title="peach.fuzzy.control.Controller.y
peach.nn.base.Layer.y
peach.nn.nnet.FeedForward.y
peach.nn.nnet.SOM.y
peach.nn.rbfn.RBFN.y" class="py-name" href="#" onclick="return doclink('link-113', 'y', 'link-107');">y</a></tt> <tt class="py-op">=</tt> <tt class="py-name">xy</tt> </tt>
<a name="L721"></a><tt class="py-lineno">721</tt>  <tt class="py-line">        <tt class="py-keyword">return</tt> <tt class="py-name">array</tt><tt class="py-op">(</tt> <tt class="py-op">[</tt> <tt class="py-op">-</tt><tt class="py-number">2.</tt><tt class="py-op">*</tt><tt class="py-op">(</tt><tt class="py-number">1.</tt><tt class="py-op">-</tt><tt id="link-114" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-114', 'x', 'link-11');">x</a></tt><tt class="py-op">)</tt> <tt class="py-op">-</tt> <tt class="py-number">4.</tt><tt class="py-op">*</tt><tt id="link-115" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-115', 'x', 'link-11');">x</a></tt><tt class="py-op">*</tt><tt class="py-op">(</tt><tt id="link-116" class="py-name"><a title="peach.fuzzy.control.Controller.y
peach.nn.base.Layer.y
peach.nn.nnet.FeedForward.y
peach.nn.nnet.SOM.y
peach.nn.rbfn.RBFN.y" class="py-name" href="#" onclick="return doclink('link-116', 'y', 'link-107');">y</a></tt> <tt class="py-op">-</tt> <tt id="link-117" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-117', 'x', 'link-11');">x</a></tt><tt class="py-op">*</tt><tt id="link-118" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-118', 'x', 'link-11');">x</a></tt><tt class="py-op">)</tt><tt class="py-op">,</tt> <tt class="py-number">2.</tt><tt class="py-op">*</tt><tt class="py-op">(</tt><tt id="link-119" class="py-name"><a title="peach.fuzzy.control.Controller.y
peach.nn.base.Layer.y
peach.nn.nnet.FeedForward.y
peach.nn.nnet.SOM.y
peach.nn.rbfn.RBFN.y" class="py-name" href="#" onclick="return doclink('link-119', 'y', 'link-107');">y</a></tt> <tt class="py-op">-</tt> <tt id="link-120" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-120', 'x', 'link-11');">x</a></tt><tt class="py-op">*</tt><tt id="link-121" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-121', 'x', 'link-11');">x</a></tt><tt class="py-op">)</tt> <tt class="py-op">]</tt><tt class="py-op">)</tt> </tt>
</div><a name="L722"></a><tt class="py-lineno">722</tt>  <tt class="py-line"> </tt>
<a name="L723"></a><tt class="py-lineno">723</tt>  <tt class="py-line">    <tt class="py-comment"># Hessian of Rosenbrock function</tt> </tt>
<a name="hf"></a><div id="hf-def"><a name="L724"></a><tt class="py-lineno">724</tt> <a class="py-toggle" href="#" id="hf-toggle" onclick="return toggle('hf');">-</a><tt class="py-line">    <tt class="py-keyword">def</tt> <a class="py-def-name" href="peach.optm.multivar-module.html#hf">hf</a><tt class="py-op">(</tt><tt class="py-param">xy</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="hf-collapsed" style="display:none;" pad="+++" indent="++++++++"></div><div id="hf-expanded"><a name="L725"></a><tt class="py-lineno">725</tt>  <tt class="py-line">        <tt id="link-122" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-122', 'x', 'link-11');">x</a></tt><tt class="py-op">,</tt> <tt id="link-123" class="py-name"><a title="peach.fuzzy.control.Controller.y
peach.nn.base.Layer.y
peach.nn.nnet.FeedForward.y
peach.nn.nnet.SOM.y
peach.nn.rbfn.RBFN.y" class="py-name" href="#" onclick="return doclink('link-123', 'y', 'link-107');">y</a></tt> <tt class="py-op">=</tt> <tt class="py-name">xy</tt> </tt>
<a name="L726"></a><tt class="py-lineno">726</tt>  <tt class="py-line">        <tt class="py-keyword">return</tt> <tt class="py-name">array</tt><tt class="py-op">(</tt><tt class="py-op">[</tt> <tt class="py-op">[</tt> <tt class="py-number">2.</tt> <tt class="py-op">-</tt> <tt class="py-number">4.</tt><tt class="py-op">*</tt><tt class="py-op">(</tt><tt id="link-124" class="py-name"><a title="peach.fuzzy.control.Controller.y
peach.nn.base.Layer.y
peach.nn.nnet.FeedForward.y
peach.nn.nnet.SOM.y
peach.nn.rbfn.RBFN.y" class="py-name" href="#" onclick="return doclink('link-124', 'y', 'link-107');">y</a></tt> <tt class="py-op">-</tt> <tt class="py-number">3.</tt><tt class="py-op">*</tt><tt id="link-125" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-125', 'x', 'link-11');">x</a></tt><tt class="py-op">*</tt><tt id="link-126" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-126', 'x', 'link-11');">x</a></tt><tt class="py-op">)</tt><tt class="py-op">,</tt> <tt class="py-op">-</tt><tt class="py-number">4.</tt><tt class="py-op">*</tt><tt id="link-127" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-127', 'x', 'link-11');">x</a></tt> <tt class="py-op">]</tt><tt class="py-op">,</tt> </tt>
<a name="L727"></a><tt class="py-lineno">727</tt>  <tt class="py-line">                       <tt class="py-op">[</tt> <tt class="py-op">-</tt><tt class="py-number">4.</tt><tt class="py-op">*</tt><tt id="link-128" class="py-name"><a title="peach.fuzzy.cmeans.FuzzyCMeans.x
peach.optm.linear.Direct1D.x
peach.optm.linear.GoldenRule.x
peach.optm.linear.Interpolation.x
peach.optm.multivar.Direct.x
peach.optm.multivar.Gradient.x
peach.optm.multivar.MomentumGradient.x
peach.optm.multivar.Newton.x
peach.optm.quasinewton.DFP.x
peach.optm.quasinewton.SR1.x
peach.sa.base.BinarySA.x
peach.sa.base.ContinuousSA.x" class="py-name" href="#" onclick="return doclink('link-128', 'x', 'link-11');">x</a></tt><tt class="py-op">,</tt> <tt class="py-number">2.</tt> <tt class="py-op">]</tt> <tt class="py-op">]</tt><tt class="py-op">)</tt> </tt>
</div><a name="L728"></a><tt class="py-lineno">728</tt>  <tt class="py-line"> </tt>
<a name="L729"></a><tt class="py-lineno">729</tt>  <tt class="py-line">    <tt id="link-129" class="py-name" targets="Module peach.optm.linear=peach.optm.linear-module.html"><a title="peach.optm.linear" class="py-name" href="#" onclick="return doclink('link-129', 'linear', 'link-129');">linear</a></tt> <tt class="py-op">=</tt> <tt id="link-130" class="py-name" targets="Class peach.optm.multivar.Direct=peach.optm.multivar.Direct-class.html"><a title="peach.optm.multivar.Direct" class="py-name" href="#" onclick="return doclink('link-130', 'Direct', 'link-130');">Direct</a></tt><tt class="py-op">(</tt><tt class="py-name">f</tt><tt class="py-op">,</tt> <tt class="py-op">(</tt><tt class="py-number">0.</tt><tt class="py-op">,</tt> <tt class="py-number">0.</tt><tt class="py-op">)</tt><tt class="py-op">,</tt> <tt class="py-name">emax</tt><tt class="py-op">=</tt><tt class="py-number">1e-12</tt><tt class="py-op">)</tt> </tt>
<a name="L730"></a><tt class="py-lineno">730</tt>  <tt class="py-line">    <tt class="py-keyword">print</tt> <tt id="link-131" class="py-name"><a title="peach.optm.linear" class="py-name" href="#" onclick="return doclink('link-131', 'linear', 'link-129');">linear</a></tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L731"></a><tt class="py-lineno">731</tt>  <tt class="py-line">    <tt class="py-name">grad</tt> <tt class="py-op">=</tt> <tt id="link-132" class="py-name" targets="Class peach.optm.multivar.Gradient=peach.optm.multivar.Gradient-class.html"><a title="peach.optm.multivar.Gradient" class="py-name" href="#" onclick="return doclink('link-132', 'Gradient', 'link-132');">Gradient</a></tt><tt class="py-op">(</tt><tt class="py-name">f</tt><tt class="py-op">,</tt> <tt class="py-op">(</tt><tt class="py-number">0.</tt><tt class="py-op">,</tt> <tt class="py-number">0.</tt><tt class="py-op">)</tt><tt class="py-op">,</tt> <tt class="py-name">df</tt><tt class="py-op">=</tt><tt class="py-name">df</tt><tt class="py-op">,</tt> <tt class="py-name">emax</tt><tt class="py-op">=</tt><tt class="py-number">1e-12</tt><tt class="py-op">)</tt> </tt>
<a name="L732"></a><tt class="py-lineno">732</tt>  <tt class="py-line">    <tt class="py-keyword">print</tt> <tt class="py-name">grad</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L733"></a><tt class="py-lineno">733</tt>  <tt class="py-line">    <tt class="py-name">grad2</tt> <tt class="py-op">=</tt> <tt id="link-133" class="py-name"><a title="peach.optm.multivar.Gradient" class="py-name" href="#" onclick="return doclink('link-133', 'Gradient', 'link-132');">Gradient</a></tt><tt class="py-op">(</tt><tt class="py-name">f</tt><tt class="py-op">,</tt> <tt class="py-op">(</tt><tt class="py-number">0.</tt><tt class="py-op">,</tt> <tt class="py-number">0.</tt><tt class="py-op">)</tt><tt class="py-op">,</tt> <tt class="py-name">emax</tt><tt class="py-op">=</tt><tt class="py-number">1e-12</tt><tt class="py-op">)</tt> </tt>
<a name="L734"></a><tt class="py-lineno">734</tt>  <tt class="py-line">    <tt class="py-keyword">print</tt> <tt class="py-name">grad2</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L735"></a><tt class="py-lineno">735</tt>  <tt class="py-line">    <tt class="py-name">mgrad</tt> <tt class="py-op">=</tt> <tt id="link-134" class="py-name" targets="Class peach.optm.multivar.MomentumGradient=peach.optm.multivar.MomentumGradient-class.html"><a title="peach.optm.multivar.MomentumGradient" class="py-name" href="#" onclick="return doclink('link-134', 'MomentumGradient', 'link-134');">MomentumGradient</a></tt><tt class="py-op">(</tt><tt class="py-name">f</tt><tt class="py-op">,</tt> <tt class="py-op">(</tt><tt class="py-number">0.</tt><tt class="py-op">,</tt> <tt class="py-number">0.</tt><tt class="py-op">)</tt><tt class="py-op">,</tt> <tt class="py-name">df</tt><tt class="py-op">=</tt><tt class="py-name">df</tt><tt class="py-op">,</tt> <tt class="py-name">emax</tt><tt class="py-op">=</tt><tt class="py-number">1e-12</tt><tt class="py-op">)</tt> </tt>
<a name="L736"></a><tt class="py-lineno">736</tt>  <tt class="py-line">    <tt class="py-keyword">print</tt> <tt class="py-name">mgrad</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L737"></a><tt class="py-lineno">737</tt>  <tt class="py-line">    <tt class="py-name">mgrad2</tt> <tt class="py-op">=</tt> <tt id="link-135" class="py-name"><a title="peach.optm.multivar.MomentumGradient" class="py-name" href="#" onclick="return doclink('link-135', 'MomentumGradient', 'link-134');">MomentumGradient</a></tt><tt class="py-op">(</tt><tt class="py-name">f</tt><tt class="py-op">,</tt> <tt class="py-op">(</tt><tt class="py-number">0.</tt><tt class="py-op">,</tt> <tt class="py-number">0.</tt><tt class="py-op">)</tt><tt class="py-op">,</tt> <tt class="py-name">emax</tt><tt class="py-op">=</tt><tt class="py-number">1e-12</tt><tt class="py-op">)</tt> </tt>
<a name="L738"></a><tt class="py-lineno">738</tt>  <tt class="py-line">    <tt class="py-keyword">print</tt> <tt class="py-name">mgrad2</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L739"></a><tt class="py-lineno">739</tt>  <tt class="py-line">    <tt class="py-name">newton</tt> <tt class="py-op">=</tt> <tt id="link-136" class="py-name" targets="Class peach.optm.multivar.Newton=peach.optm.multivar.Newton-class.html"><a title="peach.optm.multivar.Newton" class="py-name" href="#" onclick="return doclink('link-136', 'Newton', 'link-136');">Newton</a></tt><tt class="py-op">(</tt><tt class="py-name">f</tt><tt class="py-op">,</tt> <tt class="py-op">(</tt><tt class="py-number">0.</tt><tt class="py-op">,</tt> <tt class="py-number">0.</tt><tt class="py-op">)</tt><tt class="py-op">,</tt> <tt class="py-name">df</tt><tt class="py-op">=</tt><tt class="py-name">df</tt><tt class="py-op">,</tt> <tt class="py-name">hf</tt><tt class="py-op">=</tt><tt class="py-name">hf</tt><tt class="py-op">,</tt> <tt class="py-name">emax</tt><tt class="py-op">=</tt><tt class="py-number">1e-12</tt><tt class="py-op">)</tt> </tt>
<a name="L740"></a><tt class="py-lineno">740</tt>  <tt class="py-line">    <tt class="py-keyword">print</tt> <tt class="py-name">newton</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L741"></a><tt class="py-lineno">741</tt>  <tt class="py-line">    <tt class="py-name">newton2</tt> <tt class="py-op">=</tt> <tt id="link-137" class="py-name"><a title="peach.optm.multivar.Newton" class="py-name" href="#" onclick="return doclink('link-137', 'Newton', 'link-136');">Newton</a></tt><tt class="py-op">(</tt><tt class="py-name">f</tt><tt class="py-op">,</tt> <tt class="py-op">(</tt><tt class="py-number">0.</tt><tt class="py-op">,</tt> <tt class="py-number">0.</tt><tt class="py-op">)</tt><tt class="py-op">,</tt> <tt class="py-name">emax</tt><tt class="py-op">=</tt><tt class="py-number">1e-12</tt><tt class="py-op">)</tt> </tt>
<a name="L742"></a><tt class="py-lineno">742</tt>  <tt class="py-line">    <tt class="py-keyword">print</tt> <tt class="py-name">newton2</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L743"></a><tt class="py-lineno">743</tt>  <tt class="py-line"> </tt><script type="text/javascript">
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